From 2794d38f99f51cd828961c8277e5149c11bb9e8d Mon Sep 17 00:00:00 2001
From: Shuowei Li <shuowei@google.com>
Date: Fri, 5 Dec 2025 04:26:56 +0000
Subject: [PATCH 1/5] fix: Correct typo in timeseries_analysis.ipynb to resolve
 BadRequest

---
 bigframes/ml/forecasting.py               |  14 ++
 notebooks/ml/timeseries_analysis.ipynb    | 198 ++++++++++++++++++++++
 tests/system/large/ml/test_forecasting.py |  21 +++
 3 files changed, 233 insertions(+)
 create mode 100644 notebooks/ml/timeseries_analysis.ipynb

diff --git a/bigframes/ml/forecasting.py b/bigframes/ml/forecasting.py
index d26abdfa71..70688f216a 100644
--- a/bigframes/ml/forecasting.py
+++ b/bigframes/ml/forecasting.py
@@ -230,6 +230,20 @@ def _fit(
         """
         X, y = utils.batch_convert_to_dataframe(X, y)
 
+        # Auto-convert Date to datetime for hourly/per_minute frequency
+        if self.data_frequency in ["hourly", "per_minute"]:
+            timestamp_col = X.columns[0]
+            if "date" in X[timestamp_col].dtype.name:
+                import warnings
+
+                warnings.warn(
+                    f"Converting Date column '{timestamp_col}' to datetime for "
+                    f"{self.data_frequency} frequency. This is required because "
+                    f"BigQuery ML doesn't support Date type with hourly frequency."
+                )
+                X = X.copy()
+                X[timestamp_col] = bpd.to_datetime(X[timestamp_col])
+
         if X.columns.size != 1:
             raise ValueError("Time series timestamp input X contain at least 1 column.")
         if y.columns.size != 1:
diff --git a/notebooks/ml/timeseries_analysis.ipynb b/notebooks/ml/timeseries_analysis.ipynb
new file mode 100644
index 0000000000..4c714a628f
--- /dev/null
+++ b/notebooks/ml/timeseries_analysis.ipynb
@@ -0,0 +1,198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cf1403ce",
+   "metadata": {},
+   "source": [
+    "# Time Series Forecasting with BigFrames\n",
+    "\n",
+    "This notebook demonstrates time series forecasting using BigFrames with TimesFM and ARIMAPlus models on San Francisco bikeshare data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0b2db75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import bigframes.pandas as bpd\n",
+    "bpd.options.display.repr_mode = \"anywidget\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83928f4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load bikeshare data, filtering for subscriber trips from 2018 onwards.\n",
+    "df = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n",
+    "df = df[df[\"start_date\"] >= \"2018-01-01\"]\n",
+    "df = df[df[\"subscriber_type\"] == \"Subscriber\"]\n",
+    "\n",
+    "# Aggregate trips by hour.\n",
+    "df[\"trip_hour\"] = df[\"start_date\"] .dt.floor(\"h\")\n",
+    "df_grouped = df[[\"trip_hour\", \"trip_id\"]].groupby(\"trip_hour\").count().reset_index()\n",
+    "df_grouped = df_grouped.rename(columns={\"trip_id\": \"num_trips\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c43b7e65",
+   "metadata": {},
+   "source": [
+    "## Forecasting with TimesFM\n",
+    "\n",
+    "Use TimesFM to forecast the number of bikeshare trips for the last week of the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1096e154",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Forecast the last 168 hours (one week).\n",
+    "result = df_grouped.head(2842-168).ai.forecast(\n",
+    "    timestamp_column=\"trip_hour\",\n",
+    "    data_column=\"num_trips\",\n",
+    "    horizon=168\n",
+    ")\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90e80a82",
+   "metadata": {},
+   "source": [
+    "## Forecasting with ARIMAPlus\n",
+    "\n",
+    "Forecast the same period using the ARIMAPlus model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f41e1cf0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bigframes.ml import forecasting\n",
+    "\n",
+    "# Create and configure an ARIMAPlus model for hourly data.\n",
+    "model = forecasting.ARIMAPlus(\n",
+    "    auto_arima_max_order=5,  # Reduce runtime for large datasets\n",
+    "    data_frequency=\"hourly\",\n",
+    "    horizon=168\n",
+    ")\n",
+    "\n",
+    "# Use the same training data as the TimesFM model.\n",
+    "X = df_grouped.head(2842-168)[[\"trip_hour\"]]\n",
+    "y = df_grouped.head(2842-168)[[\"num_trips\"]]\n",
+    "\n",
+    "model.fit(X, y)\n",
+    "predictions = model.predict(horizon=168, confidence_level=0.95)\n",
+    "predictions\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "015804c3",
+   "metadata": {},
+   "source": [
+    "## Multiple Time Series Forecasting\n",
+    "\n",
+    "Use ARIMAPlus to forecast multiple time series simultaneously. The `id_col` parameter differentiates each series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6dbe6c48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filter for specific stations to create distinct time series.\n",
+    "df_multi = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n",
+    "df_multi = df_multi[df_multi[\"start_station_name\"] .str.contains(\"Market|Powell|Embarcadero\")]\n",
+    "\n",
+    "# Group data by station and date.\n",
+    "features = bpd.DataFrame({\n",
+    "    \"start_station_name\": df_multi[\"start_station_name\"],\n",
+    "    \"num_trips\": df_multi[\"start_date\"],\n",
+    "    \"date\": df_multi[\"start_date\"] .dt.date,\n",
+    "})\n",
+    "num_trips = features.groupby(\n",
+    "    [\"start_station_name\", \"date\"], as_index=False\n",
+    " ).count()\n",
+    "\n",
+    "# Fit the model, identifying each series by 'start_station_name'.\n",
+    "model.fit(\n",
+    "    num_trips[[\"date\"]],\n",
+    "    num_trips[[\"num_trips\"]],\n",
+    "    id_col=num_trips[[\"start_station_name\"]]\n",
+    ")\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed68c3c",
+   "metadata": {},
+   "source": [
+    "## Visualize Forecasting Results\n",
+    "\n",
+    "Plot the TimesFM forecast results against the actual data to visually assess model performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e7a29e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepare forecast data for plotting.\n",
+    "result = result.sort_values(\"forecast_timestamp\")\n",
+    "result = result[[\"forecast_timestamp\", \"forecast_value\"]]\n",
+    "result = result.rename(columns={\n",
+    "    \"forecast_timestamp\": \"trip_hour\",\n",
+    "    \"forecast_value\": \"num_trips_forecast\"\n",
+    "})\n",
+    "\n",
+    "# Combine actual and forecasted data for the last 4 weeks.\n",
+    "df_all = bpd.concat([df_grouped, result])\n",
+    "df_all = df_all.tail(672)\n",
+    "\n",
+    "# Plot actual vs. forecasted trips.\n",
+    "df_all.plot.line()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tests/system/large/ml/test_forecasting.py b/tests/system/large/ml/test_forecasting.py
index 72a0ee469b..55846d9862 100644
--- a/tests/system/large/ml/test_forecasting.py
+++ b/tests/system/large/ml/test_forecasting.py
@@ -190,3 +190,24 @@ def test_arima_plus_model_fit_params(
     assert reloaded_model.min_time_series_length == 10
     assert reloaded_model.trend_smoothing_window_size == 5
     assert reloaded_model.decompose_time_series is False
+
+
+def test_arima_plus_model_fit_date_conversion(time_series_df_default_index):
+    model = forecasting.ARIMAPlus(data_frequency="hourly")
+
+    # Arrange: Create a dataframe with a date column to test auto-conversion
+    df = time_series_df_default_index.copy()
+    df["parsed_date"] = df["parsed_date"].dt.date
+
+    X_train = df[["parsed_date"]]
+    y_train = df[["total_visits"]]
+
+    with pytest.warns(
+        UserWarning,
+        match="Converting Date column 'parsed_date' to datetime for hourly frequency.",
+    ):
+        # Act
+        model.fit(X_train, y_train)
+
+    # Assert
+    assert model._bqml_model is not None

From dc32fee5f0b0a7807dd53a1f4685adcad7fb0f9c Mon Sep 17 00:00:00 2001
From: Shuowei Li <shuowei@google.com>
Date: Fri, 5 Dec 2025 05:08:00 +0000
Subject: [PATCH 2/5] notebook update

---
 notebooks/ml/timeseries_analysis.ipynb | 1390 +++++++++++++++++++++++-
 1 file changed, 1330 insertions(+), 60 deletions(-)

diff --git a/notebooks/ml/timeseries_analysis.ipynb b/notebooks/ml/timeseries_analysis.ipynb
index 4c714a628f..25be114dbe 100644
--- a/notebooks/ml/timeseries_analysis.ipynb
+++ b/notebooks/ml/timeseries_analysis.ipynb
@@ -7,12 +7,12 @@
    "source": [
     "# Time Series Forecasting with BigFrames\n",
     "\n",
-    "This notebook demonstrates time series forecasting using BigFrames with TimesFM and ARIMAPlus models on San Francisco bikeshare data."
+    "This notebook provides a comprehensive walkthrough of time series forecasting using the BigFrames library. We will explore two powerful models, TimesFM and ARIMAPlus, to predict bikeshare trip demand based on historical data from San Francisco. The process covers data loading, preprocessing, model training, and visualization of the results."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "c0b2db75",
    "metadata": {},
    "outputs": [],
@@ -21,19 +21,31 @@
     "bpd.options.display.repr_mode = \"anywidget\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0eba46b9",
+   "metadata": {},
+   "source": [
+    "### 1. Data Loading and Preprocessing\n",
+    "\n",
+    "The first step is to load the San Francisco bikeshare dataset from BigQuery. We then preprocess the data by filtering for trips made by 'Subscriber' type users from 2018 onwards. This ensures we are working with a relevant and consistent subset of the data. Finally, we aggregate the trip data by the hour to create a time series of trip counts."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "83928f4d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load bikeshare data, filtering for subscriber trips from 2018 onwards.\n",
+    "# Load the bikeshare dataset from the public BigQuery repository.\n",
     "df = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n",
+    "\n",
+    "# Filter the data to focus on a specific time period and user type.\n",
     "df = df[df[\"start_date\"] >= \"2018-01-01\"]\n",
     "df = df[df[\"subscriber_type\"] == \"Subscriber\"]\n",
     "\n",
-    "# Aggregate trips by hour.\n",
+    "# Resample the data to an hourly frequency by counting the number of trips in each hour.\n",
     "df[\"trip_hour\"] = df[\"start_date\"] .dt.floor(\"h\")\n",
     "df_grouped = df[[\"trip_hour\", \"trip_id\"]].groupby(\"trip_hour\").count().reset_index()\n",
     "df_grouped = df_grouped.rename(columns={\"trip_id\": \"num_trips\"})"
@@ -44,19 +56,342 @@
    "id": "c43b7e65",
    "metadata": {},
    "source": [
-    "## Forecasting with TimesFM\n",
+    "### 2. Forecasting with TimesFM\n",
     "\n",
-    "Use TimesFM to forecast the number of bikeshare trips for the last week of the dataset."
+    "In this section, we use the TimesFM (Time Series Foundation Model) to forecast future bikeshare demand. TimesFM is a powerful model designed for a wide range of time series forecasting tasks. We will use it to predict the number of trips for the last week of our dataset."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "1096e154",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/dataframe.py:5329: FutureWarning: The 'ai' property will be removed. Please use 'bigframes.bigquery.ai'\n",
+      "instead.\n",
+      "  warnings.warn(msg, category=FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 58.7 MB in 17 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:e705ac1b-3671-4adb-8250-aac95e4fb2a8&page=queryresults\">Job bigframes-dev:US.e705ac1b-3671-4adb-8250-aac95e4fb2a8 details</a>]\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 7.1 kB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 7.1 kB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "841509abac424b13953a601c4602ab04",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe table table-striped table-hover\" id=\"table-1bebce70-b2ea-4047-ba97-c8484dc13354\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">forecast_timestamp</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">forecast_value</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_level</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">prediction_interval_lower_bound</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">prediction_interval_upper_bound</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">ai_forecast_status</div></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-24 14:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        126.519211\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        96.837778\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        156.200644\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        \n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-30 21:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        82.266197\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        -7.690994\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        172.223388\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        \n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-25 14:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        130.057266\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        78.019585\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        182.094948\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        \n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-26 06:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        47.235214\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        -16.565634\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        111.036063\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        \n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-28 01:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.761139\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        -61.080531\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        62.602809\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        \n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-27 11:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        160.437042\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        80.767928\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        240.106157\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        \n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-25 07:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        321.418488\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
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+       "      </td>\n",
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+       "        435.492729\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        \n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-24 16:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        284.640564\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
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+       "        370.730941\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        \n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-25 16:00:00+00:00\n",
+       "      </td>\n",
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+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        457.389023\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        \n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-26 10:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        160.995972\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        67.706721\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        254.285223\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        \n",
+       "      </td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "          forecast_timestamp  forecast_value  confidence_level  \\\n",
+       "0  2018-04-24 14:00:00+00:00      126.519211              0.95   \n",
+       "1  2018-04-30 21:00:00+00:00       82.266197              0.95   \n",
+       "2  2018-04-25 14:00:00+00:00      130.057266              0.95   \n",
+       "3  2018-04-26 06:00:00+00:00       47.235214              0.95   \n",
+       "4  2018-04-28 01:00:00+00:00        0.761139              0.95   \n",
+       "5  2018-04-27 11:00:00+00:00      160.437042              0.95   \n",
+       "6  2018-04-25 07:00:00+00:00      321.418488              0.95   \n",
+       "7  2018-04-24 16:00:00+00:00      284.640564              0.95   \n",
+       "8  2018-04-25 16:00:00+00:00      329.653748              0.95   \n",
+       "9  2018-04-26 10:00:00+00:00      160.995972              0.95   \n",
+       "\n",
+       "   prediction_interval_lower_bound  prediction_interval_upper_bound  \\\n",
+       "0                        96.837778                       156.200644   \n",
+       "1                        -7.690994                       172.223388   \n",
+       "2                        78.019585                       182.094948   \n",
+       "3                       -16.565634                       111.036063   \n",
+       "4                       -61.080531                        62.602809   \n",
+       "5                        80.767928                       240.106157   \n",
+       "6                       207.344246                       435.492729   \n",
+       "7                       198.550187                       370.730941   \n",
+       "8                       201.918472                       457.389023   \n",
+       "9                        67.706721                       254.285223   \n",
+       "\n",
+       "  ai_forecast_status  \n",
+       "0                     \n",
+       "1                     \n",
+       "2                     \n",
+       "3                     \n",
+       "4                     \n",
+       "5                     \n",
+       "6                     \n",
+       "7                     \n",
+       "8                     \n",
+       "9                     \n",
+       "...\n",
+       "\n",
+       "[168 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Forecast the last 168 hours (one week).\n",
+    "# Use the TimesFM model to forecast the last 168 hours (one week).\n",
+    "# The `timestamp_column` specifies the time index of the series.\n",
+    "# The `data_column` is the value we want to forecast.\n",
+    "# The `horizon` defines how many steps into the future to predict.\n",
     "result = df_grouped.head(2842-168).ai.forecast(\n",
     "    timestamp_column=\"trip_hour\",\n",
     "    data_column=\"num_trips\",\n",
@@ -70,107 +405,1042 @@
    "id": "90e80a82",
    "metadata": {},
    "source": [
-    "## Forecasting with ARIMAPlus\n",
+    "### 3. Forecasting with ARIMAPlus\n",
     "\n",
-    "Forecast the same period using the ARIMAPlus model."
+    "Next, we will use the ARIMAPlus model, which is a BigQuery ML model available through BigFrames. ARIMAPlus is an advanced forecasting model that can capture complex time series patterns. We will train it on the same historical data and use it to forecast the same period as the TimesFM model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "f41e1cf0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    Query processed 1.8 MB in 54 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:69146660-7957-43fc-8e91-2683b1044327&page=queryresults\">Job bigframes-dev:US.69146660-7957-43fc-8e91-2683b1044327 details</a>]\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 92.2 kB in a moment of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:e7a7c265-6eb0-4560-bb6e-ea91884fe177&page=queryresults\">Job bigframes-dev:US.e7a7c265-6eb0-4560-bb6e-ea91884fe177 details</a>]\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 1.3 kB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 10.8 kB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 0 Bytes in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "410c0f4c2d10476c8a21e917a33956f6",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe table table-striped table-hover\" id=\"table-ffb983c8-74d4-45f5-8f13-921a2afcb36f\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">forecast_timestamp</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">forecast_value</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">standard_error</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_level</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">prediction_interval_lower_bound</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">prediction_interval_upper_bound</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_interval_lower_bound</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_interval_upper_bound</div></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-24 00:00:00+00:00\n",
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+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
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+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
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+       "      </td>\n",
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+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-24 05:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        81.428432\n",
+       "      </td>\n",
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+       "      </td>\n",
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+       "      </td>\n",
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+       "      </td>\n",
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+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
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+       "      </td>\n",
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+       "      </td>\n",
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+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
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+       "      </td>\n",
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+       "      </td>\n",
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+       "        124.197176\n",
+       "      </td>\n",
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+       "        350.247546\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-24 08:00:00+00:00\n",
+       "      </td>\n",
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+       "      </td>\n",
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+       "      </td>\n",
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+       "        0.950000\n",
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+       "        208.914360\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        438.530784\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        208.914360\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        438.530784\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2018-04-24 09:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        357.288952\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        59.806906\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        240.279247\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        474.298656\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        240.279247\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        474.298656\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "          forecast_timestamp  forecast_value  standard_error  \\\n",
+       "0  2018-04-24 00:00:00+00:00       52.768335        34.87452   \n",
+       "1  2018-04-24 01:00:00+00:00         67.3281       48.075255   \n",
+       "2  2018-04-24 02:00:00+00:00       75.205573       53.910921   \n",
+       "3  2018-04-24 03:00:00+00:00       80.070922       55.994076   \n",
+       "4  2018-04-24 04:00:00+00:00       75.161779       56.583974   \n",
+       "5  2018-04-24 05:00:00+00:00       81.428432        56.85087   \n",
+       "6  2018-04-24 06:00:00+00:00      116.981445       57.180767   \n",
+       "7  2018-04-24 07:00:00+00:00      237.222361       57.770307   \n",
+       "8  2018-04-24 08:00:00+00:00      323.722572       58.681662   \n",
+       "9  2018-04-24 09:00:00+00:00      357.288952       59.806906   \n",
+       "\n",
+       "   confidence_level  prediction_interval_lower_bound  \\\n",
+       "0              0.95                       -15.462203   \n",
+       "1              0.95                       -26.729122   \n",
+       "2              0.95                       -30.268884   \n",
+       "3              0.95                       -29.479141   \n",
+       "4              0.95                       -35.542394   \n",
+       "5              0.95                       -29.797913   \n",
+       "6              0.95                         5.109671   \n",
+       "7              0.95                       124.197176   \n",
+       "8              0.95                        208.91436   \n",
+       "9              0.95                       240.279247   \n",
+       "\n",
+       "   prediction_interval_upper_bound  confidence_interval_lower_bound  \\\n",
+       "0                       120.998872                       -15.462203   \n",
+       "1                       161.385322                       -26.729122   \n",
+       "2                        180.68003                       -30.268884   \n",
+       "3                       189.620985                       -29.479141   \n",
+       "4                       185.865952                       -35.542394   \n",
+       "5                       192.654778                       -29.797913   \n",
+       "6                       228.853218                         5.109671   \n",
+       "7                       350.247546                       124.197176   \n",
+       "8                       438.530784                        208.91436   \n",
+       "9                       474.298656                       240.279247   \n",
+       "\n",
+       "   confidence_interval_upper_bound  \n",
+       "0                       120.998872  \n",
+       "1                       161.385322  \n",
+       "2                        180.68003  \n",
+       "3                       189.620985  \n",
+       "4                       185.865952  \n",
+       "5                       192.654778  \n",
+       "6                       228.853218  \n",
+       "7                       350.247546  \n",
+       "8                       438.530784  \n",
+       "9                       474.298656  \n",
+       "...\n",
+       "\n",
+       "[168 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from bigframes.ml import forecasting\n",
     "\n",
     "# Create and configure an ARIMAPlus model for hourly data.\n",
+    "# `auto_arima_max_order` is set to a lower value to reduce the training time.\n",
+    "# `data_frequency` is set to 'hourly' to match our aggregated data.\n",
     "model = forecasting.ARIMAPlus(\n",
     "    auto_arima_max_order=5,  # Reduce runtime for large datasets\n",
     "    data_frequency=\"hourly\",\n",
     "    horizon=168\n",
     ")\n",
     "\n",
-    "# Use the same training data as the TimesFM model.\n",
-    "X = df_grouped.head(2842-168)[[\"trip_hour\"]]\n",
-    "y = df_grouped.head(2842-168)[[\"num_trips\"]]\n",
+    "# Prepare the training data by excluding the last week.\n",
+    "X = df_grouped.head(2842-168)[[\"trip_hour\"] ]\n",
+    "y = df_grouped.head(2842-168)[[\"num_trips\"] ]\n",
+    "\n",
+    "# Fit the model to the training data.\n",
+    "model.fit(\n",
+    "    X, y\n",
+    ")\n",
     "\n",
-    "model.fit(X, y)\n",
+    "# Generate predictions for the specified horizon.\n",
     "predictions = model.predict(horizon=168, confidence_level=0.95)\n",
-    "predictions\n"
+    "predictions"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "015804c3",
+   "id": "ec5a4513",
    "metadata": {},
    "source": [
-    "## Multiple Time Series Forecasting\n",
+    "### 4. Compare and Visualize Forecasts\n",
     "\n",
-    "Use ARIMAPlus to forecast multiple time series simultaneously. The `id_col` parameter differentiates each series."
+    "Now we will visualize the forecasts from both TimesFM and ARIMAPlus against the actual historical data. This allows for a direct comparison of the two models' performance."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "6dbe6c48",
+   "execution_count": 5,
+   "id": "7f5b5b1e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 31.7 MB in 10 seconds of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 58.8 MB in 9 seconds of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='trip_hour'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Filter for specific stations to create distinct time series.\n",
-    "df_multi = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n",
-    "df_multi = df_multi[df_multi[\"start_station_name\"] .str.contains(\"Market|Powell|Embarcadero\")]\n",
+    "# Prepare the TimesFM forecast data.\n",
+    "timesfm_result = result.sort_values(\"forecast_timestamp\")[ [ \"forecast_timestamp\", \"forecast_value\" ] ]\n",
+    "timesfm_result = timesfm_result.rename(columns={\n",
+    "    \"forecast_timestamp\": \"trip_hour\",\n",
+    "    \"forecast_value\": \"timesfm_forecast\"\n",
+    "})\n",
     "\n",
-    "# Group data by station and date.\n",
-    "features = bpd.DataFrame({\n",
-    "    \"start_station_name\": df_multi[\"start_station_name\"],\n",
-    "    \"num_trips\": df_multi[\"start_date\"],\n",
-    "    \"date\": df_multi[\"start_date\"] .dt.date,\n",
+    "# Prepare the ARIMAPlus forecast data.\n",
+    "arimaplus_result = predictions.sort_values(\"forecast_timestamp\")[ [ \"forecast_timestamp\", \"forecast_value\" ] ]\n",
+    "arimaplus_result = arimaplus_result.rename(columns={\n",
+    "    \"forecast_timestamp\": \"trip_hour\",\n",
+    "    \"forecast_value\": \"arimaplus_forecast\"\n",
     "})\n",
-    "num_trips = features.groupby(\n",
-    "    [\"start_station_name\", \"date\"], as_index=False\n",
-    " ).count()\n",
     "\n",
-    "# Fit the model, identifying each series by 'start_station_name'.\n",
-    "model.fit(\n",
-    "    num_trips[[\"date\"]],\n",
-    "    num_trips[[\"num_trips\"]],\n",
-    "    id_col=num_trips[[\"start_station_name\"]]\n",
-    ")\n",
-    "model"
+    "# Merge the forecasts with the original data.\n",
+    "df_all = df_grouped.merge(timesfm_result, on=\"trip_hour\", how=\"left\")\n",
+    "df_all = df_all.merge(arimaplus_result, on=\"trip_hour\", how=\"left\")\n",
+    "\n",
+    "# Plot the last 4 weeks of data for comparison.\n",
+    "df_all.tail(672).plot.line(x=\"trip_hour\", y=[\"num_trips\", \"timesfm_forecast\", \"arimaplus_forecast\"])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4ed68c3c",
+   "id": "015804c3",
    "metadata": {},
    "source": [
-    "## Visualize Forecasting Results\n",
+    "### 5. Multiple Time Series Forecasting\n",
     "\n",
-    "Plot the TimesFM forecast results against the actual data to visually assess model performance."
+    "This section demonstrates a more advanced capability of ARIMAPlus: forecasting multiple time series simultaneously. This is useful when you have several independent series that you want to model together, such as trip counts from different bikeshare stations. The `id_col` parameter is key here, as it is used to differentiate between the individual time series."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "0e7a29e2",
+   "execution_count": 6,
+   "id": "6dbe6c48",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/core/log_adapter.py:182: TimeTravelCacheWarning: Reading cached table from 2025-12-05 04:49:35.785712+00:00 to avoid\n",
+      "incompatibilies with previous reads of this table. To read the latest\n",
+      "version, set `use_cache=False` or close the current session with\n",
+      "Session.close() or bigframes.pandas.close_session().\n",
+      "  return method(*args, **kwargs)\n",
+      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/ml/forecasting.py:239: UserWarning: Converting Date column 'date' to datetime for hourly frequency. This is required because BigQuery ML doesn't support Date type with hourly frequency.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    Query processed 39.4 MB in 2 hours of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:1e7e8196-4d38-4915-b6c4-64635c50ef6f&page=queryresults\">Job bigframes-dev:US.1e7e8196-4d38-4915-b6c4-64635c50ef6f details</a>]\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 32.0 MB in 6 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:baf4bfe4-39db-453d-996c-6ba9d3121b01&page=queryresults\">Job bigframes-dev:US.baf4bfe4-39db-453d-996c-6ba9d3121b01 details</a>]\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 4.6 kB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 11.5 kB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 0 Bytes in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26790935eaa74a60a72d095ccb2a14b9",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe table table-striped table-hover\" id=\"table-086c5893-71e3-4fd8-beea-9844763a3b8d\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">forecast_timestamp</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">start_station_name</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">forecast_value</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">standard_error</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_level</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">prediction_interval_lower_bound</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">prediction_interval_upper_bound</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_interval_lower_bound</div></th>\n",
+       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_interval_upper_bound</div></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2016-08-31 01:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        Beale at Market\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        28.768069\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.145449\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        28.483504\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        29.052633\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        28.483504\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        29.052633\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2016-08-31 01:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        Civic Center BART (7th at Market)\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        16.877399\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.116440\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        16.649589\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        17.105210\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        16.649589\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        17.105210\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2016-08-31 01:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        Embarcadero at Bryant\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        23.960762\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.115089\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        23.735596\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        24.185928\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        23.735596\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        24.185928\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2016-08-31 01:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        Embarcadero at Folsom\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        23.640867\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.142805\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        23.361474\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        23.920259\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        23.361474\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        23.920259\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2016-08-31 01:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        Embarcadero at Sansome\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        48.193322\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.242131\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        47.719603\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        48.667041\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        47.719603\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        48.667041\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2016-08-31 01:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        Embarcadero at Vallejo\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        16.076407\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.141602\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        15.799369\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        16.353444\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        15.799369\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        16.353444\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2016-08-31 01:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        Market at 10th\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        30.614706\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.193238\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        30.236644\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        30.992768\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        30.236644\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        30.992768\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2016-08-31 01:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        Market at 4th\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        22.765137\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.164358\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        22.443578\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        23.086695\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        22.443578\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        23.086695\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2016-08-31 01:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        Market at Sansome\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        44.291054\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.219354\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        43.861898\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        44.720210\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        43.861898\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        44.720210\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        2016-08-31 01:00:00+00:00\n",
+       "      </td>\n",
+       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
+       "        Mechanics Plaza (Market at Battery)\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        12.995115\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.005255\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        0.950000\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        12.984834\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        13.005396\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        12.984834\n",
+       "      </td>\n",
+       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
+       "        13.005396\n",
+       "      </td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "          forecast_timestamp                   start_station_name  \\\n",
+       "0  2016-08-31 01:00:00+00:00                      Beale at Market   \n",
+       "1  2016-08-31 01:00:00+00:00    Civic Center BART (7th at Market)   \n",
+       "2  2016-08-31 01:00:00+00:00                Embarcadero at Bryant   \n",
+       "3  2016-08-31 01:00:00+00:00                Embarcadero at Folsom   \n",
+       "4  2016-08-31 01:00:00+00:00               Embarcadero at Sansome   \n",
+       "5  2016-08-31 01:00:00+00:00               Embarcadero at Vallejo   \n",
+       "6  2016-08-31 01:00:00+00:00                       Market at 10th   \n",
+       "7  2016-08-31 01:00:00+00:00                        Market at 4th   \n",
+       "8  2016-08-31 01:00:00+00:00                    Market at Sansome   \n",
+       "9  2016-08-31 01:00:00+00:00  Mechanics Plaza (Market at Battery)   \n",
+       "\n",
+       "   forecast_value  standard_error  confidence_level  \\\n",
+       "0       28.768069        0.145449              0.95   \n",
+       "1       16.877399         0.11644              0.95   \n",
+       "2       23.960762        0.115089              0.95   \n",
+       "3       23.640867        0.142805              0.95   \n",
+       "4       48.193322        0.242131              0.95   \n",
+       "5       16.076407        0.141602              0.95   \n",
+       "6       30.614706        0.193238              0.95   \n",
+       "7       22.765137        0.164358              0.95   \n",
+       "8       44.291054        0.219354              0.95   \n",
+       "9       12.995115        0.005255              0.95   \n",
+       "\n",
+       "   prediction_interval_lower_bound  prediction_interval_upper_bound  \\\n",
+       "0                        28.483504                        29.052633   \n",
+       "1                        16.649589                         17.10521   \n",
+       "2                        23.735596                        24.185928   \n",
+       "3                        23.361474                        23.920259   \n",
+       "4                        47.719603                        48.667041   \n",
+       "5                        15.799369                        16.353444   \n",
+       "6                        30.236644                        30.992768   \n",
+       "7                        22.443578                        23.086695   \n",
+       "8                        43.861898                         44.72021   \n",
+       "9                        12.984834                        13.005396   \n",
+       "\n",
+       "   confidence_interval_lower_bound  confidence_interval_upper_bound  \n",
+       "0                        28.483504                        29.052633  \n",
+       "1                        16.649589                         17.10521  \n",
+       "2                        23.735596                        24.185928  \n",
+       "3                        23.361474                        23.920259  \n",
+       "4                        47.719603                        48.667041  \n",
+       "5                        15.799369                        16.353444  \n",
+       "6                        30.236644                        30.992768  \n",
+       "7                        22.443578                        23.086695  \n",
+       "8                        43.861898                         44.72021  \n",
+       "9                        12.984834                        13.005396  \n",
+       "...\n",
+       "\n",
+       "[123 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Prepare forecast data for plotting.\n",
-    "result = result.sort_values(\"forecast_timestamp\")\n",
-    "result = result[[\"forecast_timestamp\", \"forecast_value\"]]\n",
-    "result = result.rename(columns={\n",
-    "    \"forecast_timestamp\": \"trip_hour\",\n",
-    "    \"forecast_value\": \"num_trips_forecast\"\n",
+    "# Filter for specific stations to create a dataset with multiple distinct time series.\n",
+    "df_multi = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n",
+    "df_multi = df_multi[df_multi[\"start_station_name\"] .str.contains(\"Market|Powell|Embarcadero\")]\n",
+    "\n",
+    "# Group the data by station and date to create a time series for each station.\n",
+    "features = bpd.DataFrame({\n",
+    "    \"start_station_name\": df_multi[\"start_station_name\"],\n",
+    "    \"num_trips\": df_multi[\"start_date\"],\n",
+    "    \"date\": df_multi[\"start_date\"] .dt.date,\n",
     "})\n",
+    "num_trips = features.groupby(\n",
+    "    [ \"start_station_name\", \"date\" ], as_index=False\n",
+    " ).count()\n",
     "\n",
-    "# Combine actual and forecasted data for the last 4 weeks.\n",
-    "df_all = bpd.concat([df_grouped, result])\n",
-    "df_all = df_all.tail(672)\n",
+    "# Fit the model, using the 'start_station_name' column to identify each individual time series.\n",
+    "model.fit (\n",
+    "    num_trips[[\"date\"]],\n",
+    "    num_trips[[\"num_trips\"]],\n",
+    "    id_col=num_trips[[\"start_station_name\"] ]\n",
+    ")\n",
     "\n",
-    "# Plot actual vs. forecasted trips.\n",
-    "df_all.plot.line()"
+    "# Predict the future values for each time series.\n",
+    "predictions_multi = model.predict()\n",
+    "predictions_multi"
    ]
   }
  ],

From 3bec4b3ed05cac09b30e1262e1b00c5d8b8680ac Mon Sep 17 00:00:00 2001
From: Shuowei Li <shuowei@google.com>
Date: Fri, 12 Dec 2025 03:34:12 +0000
Subject: [PATCH 3/5] notebook update

---
 notebooks/ml/timeseries_analysis.ipynb | 1059 +++---------------------
 1 file changed, 99 insertions(+), 960 deletions(-)

diff --git a/notebooks/ml/timeseries_analysis.ipynb b/notebooks/ml/timeseries_analysis.ipynb
index 25be114dbe..ce078558e5 100644
--- a/notebooks/ml/timeseries_analysis.ipynb
+++ b/notebooks/ml/timeseries_analysis.ipynb
@@ -71,7 +71,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/dataframe.py:5329: FutureWarning: The 'ai' property will be removed. Please use 'bigframes.bigquery.ai'\n",
+      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/dataframe.py:5264: FutureWarning: The 'ai' property will be removed. Please use 'bigframes.bigquery.ai'\n",
       "instead.\n",
       "  warnings.warn(msg, category=FutureWarning)\n"
      ]
@@ -80,7 +80,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 58.7 MB in 17 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:e705ac1b-3671-4adb-8250-aac95e4fb2a8&page=queryresults\">Job bigframes-dev:US.e705ac1b-3671-4adb-8250-aac95e4fb2a8 details</a>]\n",
+       "    Query processed 58.7 MB in 16 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:8904286c-644a-409d-9ba0-fe308a3382bf&page=queryresults\">Job bigframes-dev:US.8904286c-644a-409d-9ba0-fe308a3382bf details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -94,7 +94,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 7.1 kB in a moment of slot time.\n",
+       "    Query processed 7.1 kB in 16 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:f859016e-cf03-4581-b176-e50c559f9380&page=queryresults\">Job bigframes-dev:US.f859016e-cf03-4581-b176-e50c559f9380 details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -108,7 +108,21 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 7.1 kB in a moment of slot time.\n",
+       "    Query processed 8.4 kB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 0 Bytes in a moment of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -121,265 +135,22 @@
     {
      "data": {
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-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">forecast_value</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_level</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">prediction_interval_lower_bound</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">prediction_interval_upper_bound</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">ai_forecast_status</div></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-24 14:00:00+00:00\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-30 21:00:00+00:00\n",
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-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
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-       "        172.223388\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-25 14:00:00+00:00\n",
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-       "        182.094948\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        \n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-26 06:00:00+00:00\n",
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-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
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-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -16.565634\n",
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-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        111.036063\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        \n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-28 01:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.761139\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
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-       "      </td>\n",
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-       "        62.602809\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        \n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-27 11:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        160.437042\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
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-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        240.106157\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        \n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-25 07:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        321.418488\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        207.344246\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        435.492729\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        \n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-24 16:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        284.640564\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        198.550187\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        370.730941\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        \n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-25 16:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        329.653748\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        201.918472\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        457.389023\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        \n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-26 10:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        160.995972\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        67.706721\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        254.285223\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        \n",
-       "      </td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>"
-      ],
       "text/plain": [
-       "          forecast_timestamp  forecast_value  confidence_level  \\\n",
-       "0  2018-04-24 14:00:00+00:00      126.519211              0.95   \n",
-       "1  2018-04-30 21:00:00+00:00       82.266197              0.95   \n",
-       "2  2018-04-25 14:00:00+00:00      130.057266              0.95   \n",
-       "3  2018-04-26 06:00:00+00:00       47.235214              0.95   \n",
-       "4  2018-04-28 01:00:00+00:00        0.761139              0.95   \n",
-       "5  2018-04-27 11:00:00+00:00      160.437042              0.95   \n",
-       "6  2018-04-25 07:00:00+00:00      321.418488              0.95   \n",
-       "7  2018-04-24 16:00:00+00:00      284.640564              0.95   \n",
-       "8  2018-04-25 16:00:00+00:00      329.653748              0.95   \n",
-       "9  2018-04-26 10:00:00+00:00      160.995972              0.95   \n",
-       "\n",
-       "   prediction_interval_lower_bound  prediction_interval_upper_bound  \\\n",
-       "0                        96.837778                       156.200644   \n",
-       "1                        -7.690994                       172.223388   \n",
-       "2                        78.019585                       182.094948   \n",
-       "3                       -16.565634                       111.036063   \n",
-       "4                       -61.080531                        62.602809   \n",
-       "5                        80.767928                       240.106157   \n",
-       "6                       207.344246                       435.492729   \n",
-       "7                       198.550187                       370.730941   \n",
-       "8                       201.918472                       457.389023   \n",
-       "9                        67.706721                       254.285223   \n",
-       "\n",
-       "  ai_forecast_status  \n",
-       "0                     \n",
-       "1                     \n",
-       "2                     \n",
-       "3                     \n",
-       "4                     \n",
-       "5                     \n",
-       "6                     \n",
-       "7                     \n",
-       "8                     \n",
-       "9                     \n",
-       "...\n",
-       "\n",
-       "[168 rows x 6 columns]"
+       "<bigframes.display.anywidget.TableWidget object at 0x7f62f2b13380>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "Computation deferred. Computation will process 7.1 kB"
       ]
      },
      "execution_count": 3,
@@ -420,7 +191,7 @@
      "data": {
       "text/html": [
        "\n",
-       "    Query processed 1.8 MB in 54 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:69146660-7957-43fc-8e91-2683b1044327&page=queryresults\">Job bigframes-dev:US.69146660-7957-43fc-8e91-2683b1044327 details</a>]\n",
+       "    Query processed 1.8 MB in 40 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:02689ad8-e003-4911-acfc-be2e5c75652d&page=queryresults\">Job bigframes-dev:US.02689ad8-e003-4911-acfc-be2e5c75652d details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -434,7 +205,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 92.2 kB in a moment of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:e7a7c265-6eb0-4560-bb6e-ea91884fe177&page=queryresults\">Job bigframes-dev:US.e7a7c265-6eb0-4560-bb6e-ea91884fe177 details</a>]\n",
+       "    Query processed 92.2 kB in a moment of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:9e29715c-0d9e-40db-8ead-d063791e61a5&page=queryresults\">Job bigframes-dev:US.9e29715c-0d9e-40db-8ead-d063791e61a5 details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -462,7 +233,21 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 10.8 kB in a moment of slot time.\n",
+       "    Query processed 10.8 kB in 16 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:bf966f53-b73a-42af-8cb7-5d2384620e3d&page=queryresults\">Job bigframes-dev:US.bf966f53-b73a-42af-8cb7-5d2384620e3d details</a>]\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 12.1 kB in a moment of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -489,339 +274,22 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "410c0f4c2d10476c8a21e917a33956f6",
+       "model_id": "a5360418703842cf9670f9d6ae77d8ee",
        "version_major": 2,
        "version_minor": 1
       },
-      "text/html": [
-       "<table border=\"1\" class=\"dataframe table table-striped table-hover\" id=\"table-ffb983c8-74d4-45f5-8f13-921a2afcb36f\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: left;\">\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">forecast_timestamp</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">forecast_value</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">standard_error</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_level</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">prediction_interval_lower_bound</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">prediction_interval_upper_bound</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_interval_lower_bound</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_interval_upper_bound</div></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-24 00:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        52.768335\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        34.874520\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -15.462203\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        120.998872\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -15.462203\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        120.998872\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-24 01:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        67.328100\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        48.075255\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -26.729122\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        161.385322\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -26.729122\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        161.385322\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-24 02:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        75.205573\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        53.910921\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -30.268884\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        180.680030\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -30.268884\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        180.680030\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-24 03:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        80.070922\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        55.994076\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -29.479141\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        189.620985\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -29.479141\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        189.620985\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-24 04:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        75.161779\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        56.583974\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -35.542394\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        185.865952\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -35.542394\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        185.865952\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-24 05:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        81.428432\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        56.850870\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -29.797913\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        192.654778\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        -29.797913\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        192.654778\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-24 06:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        116.981445\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        57.180767\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        5.109671\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        228.853218\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        5.109671\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        228.853218\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-24 07:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        237.222361\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        57.770307\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        124.197176\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        350.247546\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        124.197176\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        350.247546\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-24 08:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        323.722572\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        58.681662\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        208.914360\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        438.530784\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        208.914360\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        438.530784\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2018-04-24 09:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        357.288952\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        59.806906\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        240.279247\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        474.298656\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        240.279247\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        474.298656\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>"
-      ],
       "text/plain": [
-       "          forecast_timestamp  forecast_value  standard_error  \\\n",
-       "0  2018-04-24 00:00:00+00:00       52.768335        34.87452   \n",
-       "1  2018-04-24 01:00:00+00:00         67.3281       48.075255   \n",
-       "2  2018-04-24 02:00:00+00:00       75.205573       53.910921   \n",
-       "3  2018-04-24 03:00:00+00:00       80.070922       55.994076   \n",
-       "4  2018-04-24 04:00:00+00:00       75.161779       56.583974   \n",
-       "5  2018-04-24 05:00:00+00:00       81.428432        56.85087   \n",
-       "6  2018-04-24 06:00:00+00:00      116.981445       57.180767   \n",
-       "7  2018-04-24 07:00:00+00:00      237.222361       57.770307   \n",
-       "8  2018-04-24 08:00:00+00:00      323.722572       58.681662   \n",
-       "9  2018-04-24 09:00:00+00:00      357.288952       59.806906   \n",
-       "\n",
-       "   confidence_level  prediction_interval_lower_bound  \\\n",
-       "0              0.95                       -15.462203   \n",
-       "1              0.95                       -26.729122   \n",
-       "2              0.95                       -30.268884   \n",
-       "3              0.95                       -29.479141   \n",
-       "4              0.95                       -35.542394   \n",
-       "5              0.95                       -29.797913   \n",
-       "6              0.95                         5.109671   \n",
-       "7              0.95                       124.197176   \n",
-       "8              0.95                        208.91436   \n",
-       "9              0.95                       240.279247   \n",
-       "\n",
-       "   prediction_interval_upper_bound  confidence_interval_lower_bound  \\\n",
-       "0                       120.998872                       -15.462203   \n",
-       "1                       161.385322                       -26.729122   \n",
-       "2                        180.68003                       -30.268884   \n",
-       "3                       189.620985                       -29.479141   \n",
-       "4                       185.865952                       -35.542394   \n",
-       "5                       192.654778                       -29.797913   \n",
-       "6                       228.853218                         5.109671   \n",
-       "7                       350.247546                       124.197176   \n",
-       "8                       438.530784                        208.91436   \n",
-       "9                       474.298656                       240.279247   \n",
-       "\n",
-       "   confidence_interval_upper_bound  \n",
-       "0                       120.998872  \n",
-       "1                       161.385322  \n",
-       "2                        180.68003  \n",
-       "3                       189.620985  \n",
-       "4                       185.865952  \n",
-       "5                       192.654778  \n",
-       "6                       228.853218  \n",
-       "7                       350.247546  \n",
-       "8                       438.530784  \n",
-       "9                       474.298656  \n",
-       "...\n",
-       "\n",
-       "[168 rows x 8 columns]"
+       "<bigframes.display.anywidget.TableWidget object at 0x7f62f011fc50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "Computation deferred. Computation will process 10.8 kB"
       ]
      },
      "execution_count": 4,
@@ -875,7 +343,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 31.7 MB in 10 seconds of slot time.\n",
+       "    Query processed 31.7 MB in 9 seconds of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -902,7 +370,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='trip_hour'>"
+       "<Axes: title={'center': 'Trip Forecasts Comparison'}, xlabel='trip_hour'>"
       ]
      },
      "execution_count": 5,
@@ -911,7 +379,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -940,7 +408,12 @@
     "df_all = df_all.merge(arimaplus_result, on=\"trip_hour\", how=\"left\")\n",
     "\n",
     "# Plot the last 4 weeks of data for comparison.\n",
-    "df_all.tail(672).plot.line(x=\"trip_hour\", y=[\"num_trips\", \"timesfm_forecast\", \"arimaplus_forecast\"])"
+    "df_all.tail(672).plot.line(  \n",
+    "    x=\"trip_hour\",   \n",
+    "    y=[\"num_trips\", \"timesfm_forecast\", \"arimaplus_forecast\"],   \n",
+    "    rot=45,  \n",
+    "    title=\"Trip Forecasts Comparison\"  \n",
+    ")"
    ]
   },
   {
@@ -963,7 +436,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/core/log_adapter.py:182: TimeTravelCacheWarning: Reading cached table from 2025-12-05 04:49:35.785712+00:00 to avoid\n",
+      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/core/log_adapter.py:182: TimeTravelCacheWarning: Reading cached table from 2025-12-12 03:22:23.615364+00:00 to avoid\n",
       "incompatibilies with previous reads of this table. To read the latest\n",
       "version, set `use_cache=False` or close the current session with\n",
       "Session.close() or bigframes.pandas.close_session().\n",
@@ -976,7 +449,7 @@
      "data": {
       "text/html": [
        "\n",
-       "    Query processed 39.4 MB in 2 hours of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:1e7e8196-4d38-4915-b6c4-64635c50ef6f&page=queryresults\">Job bigframes-dev:US.1e7e8196-4d38-4915-b6c4-64635c50ef6f details</a>]\n",
+       "    Query processed 39.4 MB in 2 hours of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:c6f5d199-d64a-495f-b9f7-d6eaef4eb4b6&page=queryresults\">Job bigframes-dev:US.c6f5d199-d64a-495f-b9f7-d6eaef4eb4b6 details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -990,7 +463,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 32.0 MB in 6 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:baf4bfe4-39db-453d-996c-6ba9d3121b01&page=queryresults\">Job bigframes-dev:US.baf4bfe4-39db-453d-996c-6ba9d3121b01 details</a>]\n",
+       "    Query processed 32.0 MB in 5 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:83063588-a945-4405-812a-87305cf640fe&page=queryresults\">Job bigframes-dev:US.83063588-a945-4405-812a-87305cf640fe details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -1018,7 +491,21 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 11.5 kB in a moment of slot time.\n",
+       "    Query processed 11.5 kB in 11 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:2c711378-2060-46a4-90b3-183a855463d4&page=queryresults\">Job bigframes-dev:US.2c711378-2060-46a4-90b3-183a855463d4 details</a>]\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 12.4 kB in a moment of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -1045,370 +532,22 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "26790935eaa74a60a72d095ccb2a14b9",
+       "model_id": "fc8931fb4cd0464ea1ebd790eda7d909",
        "version_major": 2,
        "version_minor": 1
       },
-      "text/html": [
-       "<table border=\"1\" class=\"dataframe table table-striped table-hover\" id=\"table-086c5893-71e3-4fd8-beea-9844763a3b8d\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: left;\">\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">forecast_timestamp</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">start_station_name</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">forecast_value</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">standard_error</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_level</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">prediction_interval_lower_bound</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">prediction_interval_upper_bound</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_interval_lower_bound</div></th>\n",
-       "      <th style=\"text-align: left;\" class=\"sortable\"><div style=\"resize: horizontal; overflow: auto; box-sizing: border-box; width: 100%; height: 100%; padding: 0.5em;\">confidence_interval_upper_bound</div></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2016-08-31 01:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        Beale at Market\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        28.768069\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.145449\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        28.483504\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        29.052633\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        28.483504\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        29.052633\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2016-08-31 01:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        Civic Center BART (7th at Market)\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        16.877399\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.116440\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        16.649589\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        17.105210\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        16.649589\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        17.105210\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2016-08-31 01:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        Embarcadero at Bryant\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        23.960762\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.115089\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        23.735596\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        24.185928\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        23.735596\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        24.185928\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2016-08-31 01:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        Embarcadero at Folsom\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        23.640867\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.142805\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        23.361474\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        23.920259\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        23.361474\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        23.920259\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2016-08-31 01:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        Embarcadero at Sansome\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        48.193322\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.242131\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        47.719603\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        48.667041\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        47.719603\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        48.667041\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2016-08-31 01:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        Embarcadero at Vallejo\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        16.076407\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.141602\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        15.799369\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        16.353444\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        15.799369\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        16.353444\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2016-08-31 01:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        Market at 10th\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        30.614706\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.193238\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        30.236644\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        30.992768\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        30.236644\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        30.992768\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2016-08-31 01:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        Market at 4th\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        22.765137\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.164358\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        22.443578\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        23.086695\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        22.443578\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        23.086695\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2016-08-31 01:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        Market at Sansome\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        44.291054\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.219354\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        43.861898\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        44.720210\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        43.861898\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        44.720210\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        2016-08-31 01:00:00+00:00\n",
-       "      </td>\n",
-       "      <td style=\"text-align: left; padding: 0.5em;\">\n",
-       "        Mechanics Plaza (Market at Battery)\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        12.995115\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.005255\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        0.950000\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        12.984834\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        13.005396\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        12.984834\n",
-       "      </td>\n",
-       "      <td style=\"text-align: right; padding: 0.5em;\">\n",
-       "        13.005396\n",
-       "      </td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>"
-      ],
       "text/plain": [
-       "          forecast_timestamp                   start_station_name  \\\n",
-       "0  2016-08-31 01:00:00+00:00                      Beale at Market   \n",
-       "1  2016-08-31 01:00:00+00:00    Civic Center BART (7th at Market)   \n",
-       "2  2016-08-31 01:00:00+00:00                Embarcadero at Bryant   \n",
-       "3  2016-08-31 01:00:00+00:00                Embarcadero at Folsom   \n",
-       "4  2016-08-31 01:00:00+00:00               Embarcadero at Sansome   \n",
-       "5  2016-08-31 01:00:00+00:00               Embarcadero at Vallejo   \n",
-       "6  2016-08-31 01:00:00+00:00                       Market at 10th   \n",
-       "7  2016-08-31 01:00:00+00:00                        Market at 4th   \n",
-       "8  2016-08-31 01:00:00+00:00                    Market at Sansome   \n",
-       "9  2016-08-31 01:00:00+00:00  Mechanics Plaza (Market at Battery)   \n",
-       "\n",
-       "   forecast_value  standard_error  confidence_level  \\\n",
-       "0       28.768069        0.145449              0.95   \n",
-       "1       16.877399         0.11644              0.95   \n",
-       "2       23.960762        0.115089              0.95   \n",
-       "3       23.640867        0.142805              0.95   \n",
-       "4       48.193322        0.242131              0.95   \n",
-       "5       16.076407        0.141602              0.95   \n",
-       "6       30.614706        0.193238              0.95   \n",
-       "7       22.765137        0.164358              0.95   \n",
-       "8       44.291054        0.219354              0.95   \n",
-       "9       12.995115        0.005255              0.95   \n",
-       "\n",
-       "   prediction_interval_lower_bound  prediction_interval_upper_bound  \\\n",
-       "0                        28.483504                        29.052633   \n",
-       "1                        16.649589                         17.10521   \n",
-       "2                        23.735596                        24.185928   \n",
-       "3                        23.361474                        23.920259   \n",
-       "4                        47.719603                        48.667041   \n",
-       "5                        15.799369                        16.353444   \n",
-       "6                        30.236644                        30.992768   \n",
-       "7                        22.443578                        23.086695   \n",
-       "8                        43.861898                         44.72021   \n",
-       "9                        12.984834                        13.005396   \n",
-       "\n",
-       "   confidence_interval_lower_bound  confidence_interval_upper_bound  \n",
-       "0                        28.483504                        29.052633  \n",
-       "1                        16.649589                         17.10521  \n",
-       "2                        23.735596                        24.185928  \n",
-       "3                        23.361474                        23.920259  \n",
-       "4                        47.719603                        48.667041  \n",
-       "5                        15.799369                        16.353444  \n",
-       "6                        30.236644                        30.992768  \n",
-       "7                        22.443578                        23.086695  \n",
-       "8                        43.861898                         44.72021  \n",
-       "9                        12.984834                        13.005396  \n",
-       "...\n",
-       "\n",
-       "[123 rows x 9 columns]"
+       "<bigframes.display.anywidget.TableWidget object at 0x7f62f0068550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "Computation deferred. Computation will process 11.5 kB"
       ]
      },
      "execution_count": 6,
@@ -1460,7 +599,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,

From 51b336bdae45ccf9c1935d977708d6910747f875 Mon Sep 17 00:00:00 2001
From: Shuowei Li <shuowei@google.com>
Date: Fri, 12 Dec 2025 04:03:20 +0000
Subject: [PATCH 4/5] minor update

---
 bigframes/ml/forecasting.py               |   3 +-
 notebooks/ml/timeseries_analysis.ipynb    | 103 ++++++++--------------
 tests/system/large/ml/test_forecasting.py |   6 +-
 3 files changed, 39 insertions(+), 73 deletions(-)

diff --git a/bigframes/ml/forecasting.py b/bigframes/ml/forecasting.py
index 70688f216a..c39915d3e1 100644
--- a/bigframes/ml/forecasting.py
+++ b/bigframes/ml/forecasting.py
@@ -17,6 +17,7 @@
 from __future__ import annotations
 
 from typing import List, Optional
+import warnings
 
 from google.cloud import bigquery
 
@@ -234,8 +235,6 @@ def _fit(
         if self.data_frequency in ["hourly", "per_minute"]:
             timestamp_col = X.columns[0]
             if "date" in X[timestamp_col].dtype.name:
-                import warnings
-
                 warnings.warn(
                     f"Converting Date column '{timestamp_col}' to datetime for "
                     f"{self.data_frequency} frequency. This is required because "
diff --git a/notebooks/ml/timeseries_analysis.ipynb b/notebooks/ml/timeseries_analysis.ipynb
index ce078558e5..51ac6279c1 100644
--- a/notebooks/ml/timeseries_analysis.ipynb
+++ b/notebooks/ml/timeseries_analysis.ipynb
@@ -18,6 +18,7 @@
    "outputs": [],
    "source": [
     "import bigframes.pandas as bpd\n",
+    "from bigframes.ml import forecasting\n",
     "bpd.options.display.repr_mode = \"anywidget\""
    ]
   },
@@ -38,15 +39,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load the bikeshare dataset from the public BigQuery repository.\n",
     "df = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n",
-    "\n",
-    "# Filter the data to focus on a specific time period and user type.\n",
     "df = df[df[\"start_date\"] >= \"2018-01-01\"]\n",
     "df = df[df[\"subscriber_type\"] == \"Subscriber\"]\n",
-    "\n",
-    "# Resample the data to an hourly frequency by counting the number of trips in each hour.\n",
-    "df[\"trip_hour\"] = df[\"start_date\"] .dt.floor(\"h\")\n",
+    "df[\"trip_hour\"] = df[\"start_date\"].dt.floor(\"h\")\n",
     "df_grouped = df[[\"trip_hour\", \"trip_id\"]].groupby(\"trip_hour\").count().reset_index()\n",
     "df_grouped = df_grouped.rename(columns={\"trip_id\": \"num_trips\"})"
    ]
@@ -80,7 +76,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 58.7 MB in 16 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:8904286c-644a-409d-9ba0-fe308a3382bf&page=queryresults\">Job bigframes-dev:US.8904286c-644a-409d-9ba0-fe308a3382bf details</a>]\n",
+       "    Query processed 58.7 MB in 16 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:b91a9e6f-d00a-44f6-afe1-255e25945a1d&page=queryresults\">Job bigframes-dev:US.b91a9e6f-d00a-44f6-afe1-255e25945a1d details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -94,7 +90,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 7.1 kB in 16 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:f859016e-cf03-4581-b176-e50c559f9380&page=queryresults\">Job bigframes-dev:US.f859016e-cf03-4581-b176-e50c559f9380 details</a>]\n",
+       "    Query processed 7.1 kB in 9 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:1f3bfd8b-5740-4895-be14-6a8b92a4f3b1&page=queryresults\">Job bigframes-dev:US.1f3bfd8b-5740-4895-be14-6a8b92a4f3b1 details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -135,12 +131,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f53e2ae48801458ab42facd6ebe10728",
+       "model_id": "8a4d64e6cf4844018c1b8593d1d99e05",
        "version_major": 2,
        "version_minor": 1
       },
       "text/plain": [
-       "<bigframes.display.anywidget.TableWidget object at 0x7f62f2b13380>"
+       "<bigframes.display.anywidget.TableWidget object at 0x7efcefb3bb60>"
       ]
      },
      "metadata": {},
@@ -159,10 +155,6 @@
     }
    ],
    "source": [
-    "# Use the TimesFM model to forecast the last 168 hours (one week).\n",
-    "# The `timestamp_column` specifies the time index of the series.\n",
-    "# The `data_column` is the value we want to forecast.\n",
-    "# The `horizon` defines how many steps into the future to predict.\n",
     "result = df_grouped.head(2842-168).ai.forecast(\n",
     "    timestamp_column=\"trip_hour\",\n",
     "    data_column=\"num_trips\",\n",
@@ -191,7 +183,7 @@
      "data": {
       "text/html": [
        "\n",
-       "    Query processed 1.8 MB in 40 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:02689ad8-e003-4911-acfc-be2e5c75652d&page=queryresults\">Job bigframes-dev:US.02689ad8-e003-4911-acfc-be2e5c75652d details</a>]\n",
+       "    Query processed 1.8 MB in 47 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:36efa98e-2843-4bc9-8225-06875236ef17&page=queryresults\">Job bigframes-dev:US.36efa98e-2843-4bc9-8225-06875236ef17 details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -205,7 +197,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 92.2 kB in a moment of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:9e29715c-0d9e-40db-8ead-d063791e61a5&page=queryresults\">Job bigframes-dev:US.9e29715c-0d9e-40db-8ead-d063791e61a5 details</a>]\n",
+       "    Query processed 92.2 kB in a moment of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:18805b62-a8e2-4c69-a5bf-97aa19df8095&page=queryresults\">Job bigframes-dev:US.18805b62-a8e2-4c69-a5bf-97aa19df8095 details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -233,7 +225,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 10.8 kB in 16 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:bf966f53-b73a-42af-8cb7-5d2384620e3d&page=queryresults\">Job bigframes-dev:US.bf966f53-b73a-42af-8cb7-5d2384620e3d details</a>]\n",
+       "    Query processed 10.8 kB in 11 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:a2b15286-6c7f-40a7-8009-045e5e4f3dbf&page=queryresults\">Job bigframes-dev:US.a2b15286-6c7f-40a7-8009-045e5e4f3dbf details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -274,12 +266,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a5360418703842cf9670f9d6ae77d8ee",
+       "model_id": "b3816017ab4440c7bcf258df4a0ceff8",
        "version_major": 2,
        "version_minor": 1
       },
       "text/plain": [
-       "<bigframes.display.anywidget.TableWidget object at 0x7f62f011fc50>"
+       "<bigframes.display.anywidget.TableWidget object at 0x7efcec227c50>"
       ]
      },
      "metadata": {},
@@ -298,27 +290,16 @@
     }
    ],
    "source": [
-    "from bigframes.ml import forecasting\n",
-    "\n",
-    "# Create and configure an ARIMAPlus model for hourly data.\n",
-    "# `auto_arima_max_order` is set to a lower value to reduce the training time.\n",
-    "# `data_frequency` is set to 'hourly' to match our aggregated data.\n",
     "model = forecasting.ARIMAPlus(\n",
     "    auto_arima_max_order=5,  # Reduce runtime for large datasets\n",
     "    data_frequency=\"hourly\",\n",
     "    horizon=168\n",
     ")\n",
-    "\n",
-    "# Prepare the training data by excluding the last week.\n",
-    "X = df_grouped.head(2842-168)[[\"trip_hour\"] ]\n",
-    "y = df_grouped.head(2842-168)[[\"num_trips\"] ]\n",
-    "\n",
-    "# Fit the model to the training data.\n",
+    "X = df_grouped.head(2842-168)[[\"trip_hour\"]]\n",
+    "y = df_grouped.head(2842-168)[[\"num_trips\"]]\n",
     "model.fit(\n",
     "    X, y\n",
     ")\n",
-    "\n",
-    "# Generate predictions for the specified horizon.\n",
     "predictions = model.predict(horizon=168, confidence_level=0.95)\n",
     "predictions"
    ]
@@ -343,7 +324,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 31.7 MB in 9 seconds of slot time.\n",
+       "    Query processed 31.7 MB in 10 seconds of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -357,7 +338,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 58.8 MB in 9 seconds of slot time.\n",
+       "    Query processed 58.8 MB in 7 seconds of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -389,30 +370,23 @@
     }
    ],
    "source": [
-    "# Prepare the TimesFM forecast data.\n",
-    "timesfm_result = result.sort_values(\"forecast_timestamp\")[ [ \"forecast_timestamp\", \"forecast_value\" ] ]\n",
+    "timesfm_result = result.sort_values(\"forecast_timestamp\")[[\"forecast_timestamp\", \"forecast_value\"]]\n",
     "timesfm_result = timesfm_result.rename(columns={\n",
     "    \"forecast_timestamp\": \"trip_hour\",\n",
     "    \"forecast_value\": \"timesfm_forecast\"\n",
     "})\n",
-    "\n",
-    "# Prepare the ARIMAPlus forecast data.\n",
-    "arimaplus_result = predictions.sort_values(\"forecast_timestamp\")[ [ \"forecast_timestamp\", \"forecast_value\" ] ]\n",
+    "arimaplus_result = predictions.sort_values(\"forecast_timestamp\")[[\"forecast_timestamp\", \"forecast_value\"]]\n",
     "arimaplus_result = arimaplus_result.rename(columns={\n",
     "    \"forecast_timestamp\": \"trip_hour\",\n",
     "    \"forecast_value\": \"arimaplus_forecast\"\n",
     "})\n",
-    "\n",
-    "# Merge the forecasts with the original data.\n",
     "df_all = df_grouped.merge(timesfm_result, on=\"trip_hour\", how=\"left\")\n",
     "df_all = df_all.merge(arimaplus_result, on=\"trip_hour\", how=\"left\")\n",
-    "\n",
-    "# Plot the last 4 weeks of data for comparison.\n",
-    "df_all.tail(672).plot.line(  \n",
-    "    x=\"trip_hour\",   \n",
-    "    y=[\"num_trips\", \"timesfm_forecast\", \"arimaplus_forecast\"],   \n",
-    "    rot=45,  \n",
-    "    title=\"Trip Forecasts Comparison\"  \n",
+    "df_all.tail(672).plot.line(\n",
+    "    x=\"trip_hour\",\n",
+    "    y=[\"num_trips\", \"timesfm_forecast\", \"arimaplus_forecast\"],\n",
+    "    rot=45,\n",
+    "    title=\"Trip Forecasts Comparison\"\n",
     ")"
    ]
   },
@@ -436,12 +410,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/core/log_adapter.py:182: TimeTravelCacheWarning: Reading cached table from 2025-12-12 03:22:23.615364+00:00 to avoid\n",
+      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/core/log_adapter.py:182: TimeTravelCacheWarning: Reading cached table from 2025-12-12 03:47:11.144938+00:00 to avoid\n",
       "incompatibilies with previous reads of this table. To read the latest\n",
       "version, set `use_cache=False` or close the current session with\n",
       "Session.close() or bigframes.pandas.close_session().\n",
       "  return method(*args, **kwargs)\n",
-      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/ml/forecasting.py:239: UserWarning: Converting Date column 'date' to datetime for hourly frequency. This is required because BigQuery ML doesn't support Date type with hourly frequency.\n",
+      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/ml/forecasting.py:238: UserWarning: Converting Date column 'date' to datetime for hourly frequency. This is required because BigQuery ML doesn't support Date type with hourly frequency.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -449,7 +423,7 @@
      "data": {
       "text/html": [
        "\n",
-       "    Query processed 39.4 MB in 2 hours of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:c6f5d199-d64a-495f-b9f7-d6eaef4eb4b6&page=queryresults\">Job bigframes-dev:US.c6f5d199-d64a-495f-b9f7-d6eaef4eb4b6 details</a>]\n",
+       "    Query processed 39.4 MB in 2 hours of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:16d735c6-c885-447f-b513-5249ee8cb48a&page=queryresults\">Job bigframes-dev:US.16d735c6-c885-447f-b513-5249ee8cb48a details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -463,7 +437,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 32.0 MB in 5 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:83063588-a945-4405-812a-87305cf640fe&page=queryresults\">Job bigframes-dev:US.83063588-a945-4405-812a-87305cf640fe details</a>]\n",
+       "    Query processed 32.0 MB in 3 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:c4cf8019-bdf6-461a-8753-e7314b837c29&page=queryresults\">Job bigframes-dev:US.c4cf8019-bdf6-461a-8753-e7314b837c29 details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -491,7 +465,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 11.5 kB in 11 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:2c711378-2060-46a4-90b3-183a855463d4&page=queryresults\">Job bigframes-dev:US.2c711378-2060-46a4-90b3-183a855463d4 details</a>]\n",
+       "    Query processed 11.5 kB in 8 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:13663bcd-b3e2-471c-b7e7-50c260c4cfdd&page=queryresults\">Job bigframes-dev:US.13663bcd-b3e2-471c-b7e7-50c260c4cfdd details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -532,12 +506,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fc8931fb4cd0464ea1ebd790eda7d909",
+       "model_id": "5cd08895656f45f5bfe39fd6bb26855f",
        "version_major": 2,
        "version_minor": 1
       },
       "text/plain": [
-       "<bigframes.display.anywidget.TableWidget object at 0x7f62f0068550>"
+       "<bigframes.display.anywidget.TableWidget object at 0x7efcec160550>"
       ]
      },
      "metadata": {},
@@ -556,28 +530,21 @@
     }
    ],
    "source": [
-    "# Filter for specific stations to create a dataset with multiple distinct time series.\n",
     "df_multi = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n",
-    "df_multi = df_multi[df_multi[\"start_station_name\"] .str.contains(\"Market|Powell|Embarcadero\")]\n",
-    "\n",
-    "# Group the data by station and date to create a time series for each station.\n",
+    "df_multi = df_multi[df_multi[\"start_station_name\"].str.contains(\"Market|Powell|Embarcadero\")]\n",
     "features = bpd.DataFrame({\n",
     "    \"start_station_name\": df_multi[\"start_station_name\"],\n",
     "    \"num_trips\": df_multi[\"start_date\"],\n",
-    "    \"date\": df_multi[\"start_date\"] .dt.date,\n",
+    "    \"date\": df_multi[\"start_date\"].dt.date,\n",
     "})\n",
     "num_trips = features.groupby(\n",
-    "    [ \"start_station_name\", \"date\" ], as_index=False\n",
-    " ).count()\n",
-    "\n",
-    "# Fit the model, using the 'start_station_name' column to identify each individual time series.\n",
-    "model.fit (\n",
+    "    [\"start_station_name\", \"date\"], as_index=False\n",
+    ").count()\n",
+    "model.fit(\n",
     "    num_trips[[\"date\"]],\n",
     "    num_trips[[\"num_trips\"]],\n",
-    "    id_col=num_trips[[\"start_station_name\"] ]\n",
+    "    id_col=num_trips[[\"start_station_name\"]]\n",
     ")\n",
-    "\n",
-    "# Predict the future values for each time series.\n",
     "predictions_multi = model.predict()\n",
     "predictions_multi"
    ]
diff --git a/tests/system/large/ml/test_forecasting.py b/tests/system/large/ml/test_forecasting.py
index 55846d9862..789068001f 100644
--- a/tests/system/large/ml/test_forecasting.py
+++ b/tests/system/large/ml/test_forecasting.py
@@ -17,7 +17,7 @@
 from bigframes.ml import forecasting
 from bigframes.testing import utils
 
-ARIMA_EVALUATE_OUTPUT_COL = [
+ARIMA_EVALUATE_OUTPUT_COLUMNS = [
     "non_seasonal_p",
     "non_seasonal_d",
     "non_seasonal_q",
@@ -106,9 +106,9 @@ def test_arima_plus_model_fit_summary(
     curr_model = arima_model_w_id if id_col_name else arima_model
     result = curr_model.summary().to_pandas()
     expected_columns = (
-        [id_col_name] + ARIMA_EVALUATE_OUTPUT_COL
+        [id_col_name] + ARIMA_EVALUATE_OUTPUT_COLUMNS
         if id_col_name
-        else ARIMA_EVALUATE_OUTPUT_COL
+        else ARIMA_EVALUATE_OUTPUT_COLUMNS
     )
     utils.check_pandas_df_schema_and_index(
         result, columns=expected_columns, index=2 if id_col_name else 1

From 85b99b3355210929e3f2a6a1b578f5b02daac37e Mon Sep 17 00:00:00 2001
From: Shuowei Li <shuowei@google.com>
Date: Fri, 12 Dec 2025 23:17:15 +0000
Subject: [PATCH 5/5] Revert code change

---
 bigframes/ml/forecasting.py               |  13 -
 notebooks/ml/timeseries_analysis.ipynb    | 731 +++++++++++++++++++---
 tests/system/large/ml/test_forecasting.py |  27 +-
 3 files changed, 649 insertions(+), 122 deletions(-)

diff --git a/bigframes/ml/forecasting.py b/bigframes/ml/forecasting.py
index c39915d3e1..d26abdfa71 100644
--- a/bigframes/ml/forecasting.py
+++ b/bigframes/ml/forecasting.py
@@ -17,7 +17,6 @@
 from __future__ import annotations
 
 from typing import List, Optional
-import warnings
 
 from google.cloud import bigquery
 
@@ -231,18 +230,6 @@ def _fit(
         """
         X, y = utils.batch_convert_to_dataframe(X, y)
 
-        # Auto-convert Date to datetime for hourly/per_minute frequency
-        if self.data_frequency in ["hourly", "per_minute"]:
-            timestamp_col = X.columns[0]
-            if "date" in X[timestamp_col].dtype.name:
-                warnings.warn(
-                    f"Converting Date column '{timestamp_col}' to datetime for "
-                    f"{self.data_frequency} frequency. This is required because "
-                    f"BigQuery ML doesn't support Date type with hourly frequency."
-                )
-                X = X.copy()
-                X[timestamp_col] = bpd.to_datetime(X[timestamp_col])
-
         if X.columns.size != 1:
             raise ValueError("Time series timestamp input X contain at least 1 column.")
         if y.columns.size != 1:
diff --git a/notebooks/ml/timeseries_analysis.ipynb b/notebooks/ml/timeseries_analysis.ipynb
index 51ac6279c1..01c5a20efa 100644
--- a/notebooks/ml/timeseries_analysis.ipynb
+++ b/notebooks/ml/timeseries_analysis.ipynb
@@ -67,7 +67,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/dataframe.py:5264: FutureWarning: The 'ai' property will be removed. Please use 'bigframes.bigquery.ai'\n",
+      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/dataframe.py:5340: FutureWarning: The 'ai' property will be removed. Please use 'bigframes.bigquery.ai'\n",
       "instead.\n",
       "  warnings.warn(msg, category=FutureWarning)\n"
      ]
@@ -76,7 +76,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 58.7 MB in 16 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:b91a9e6f-d00a-44f6-afe1-255e25945a1d&page=queryresults\">Job bigframes-dev:US.b91a9e6f-d00a-44f6-afe1-255e25945a1d details</a>]\n",
+       "    Query processed 58.7 MB in 19 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:eb026c28-038a-4ca7-acfa-474ed0be4119&page=queryresults\">Job bigframes-dev:US.eb026c28-038a-4ca7-acfa-474ed0be4119 details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -90,7 +90,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 7.1 kB in 9 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:1f3bfd8b-5740-4895-be14-6a8b92a4f3b1&page=queryresults\">Job bigframes-dev:US.1f3bfd8b-5740-4895-be14-6a8b92a4f3b1 details</a>]\n",
+       "    Query processed 7.1 kB in a moment of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -104,21 +104,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 8.4 kB in a moment of slot time.\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "✅ Completed. \n",
-       "    Query processed 0 Bytes in a moment of slot time.\n",
+       "    Query processed 7.1 kB in a moment of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -131,22 +117,172 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8a4d64e6cf4844018c1b8593d1d99e05",
+       "model_id": "929eda852e564b799cf76e62d9f7b46a",
        "version_major": 2,
        "version_minor": 1
       },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>forecast_timestamp</th>\n",
+       "      <th>forecast_value</th>\n",
+       "      <th>confidence_level</th>\n",
+       "      <th>prediction_interval_lower_bound</th>\n",
+       "      <th>prediction_interval_upper_bound</th>\n",
+       "      <th>ai_forecast_status</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-04-24 14:00:00+00:00</td>\n",
+       "      <td>126.519211</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>96.837778</td>\n",
+       "      <td>156.200644</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2018-04-30 21:00:00+00:00</td>\n",
+       "      <td>82.266197</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-7.690994</td>\n",
+       "      <td>172.223388</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2018-04-25 14:00:00+00:00</td>\n",
+       "      <td>130.057266</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>78.019585</td>\n",
+       "      <td>182.094948</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2018-04-26 06:00:00+00:00</td>\n",
+       "      <td>47.235214</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-16.565634</td>\n",
+       "      <td>111.036063</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2018-04-28 01:00:00+00:00</td>\n",
+       "      <td>0.761139</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-61.080531</td>\n",
+       "      <td>62.602809</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2018-04-27 11:00:00+00:00</td>\n",
+       "      <td>160.437042</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>80.767928</td>\n",
+       "      <td>240.106157</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2018-04-25 07:00:00+00:00</td>\n",
+       "      <td>321.418488</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>207.344246</td>\n",
+       "      <td>435.492729</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2018-04-24 16:00:00+00:00</td>\n",
+       "      <td>284.640564</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>198.550187</td>\n",
+       "      <td>370.730941</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2018-04-25 16:00:00+00:00</td>\n",
+       "      <td>329.653748</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>201.918472</td>\n",
+       "      <td>457.389023</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2018-04-26 10:00:00+00:00</td>\n",
+       "      <td>160.995972</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>67.706721</td>\n",
+       "      <td>254.285223</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 6 columns</p>\n",
+       "</div>[168 rows x 6 columns in total]"
+      ],
       "text/plain": [
-       "<bigframes.display.anywidget.TableWidget object at 0x7efcefb3bb60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "Computation deferred. Computation will process 7.1 kB"
+       "          forecast_timestamp  forecast_value  confidence_level  \\\n",
+       "0  2018-04-24 14:00:00+00:00      126.519211              0.95   \n",
+       "1  2018-04-30 21:00:00+00:00       82.266197              0.95   \n",
+       "2  2018-04-25 14:00:00+00:00      130.057266              0.95   \n",
+       "3  2018-04-26 06:00:00+00:00       47.235214              0.95   \n",
+       "4  2018-04-28 01:00:00+00:00        0.761139              0.95   \n",
+       "5  2018-04-27 11:00:00+00:00      160.437042              0.95   \n",
+       "6  2018-04-25 07:00:00+00:00      321.418488              0.95   \n",
+       "7  2018-04-24 16:00:00+00:00      284.640564              0.95   \n",
+       "8  2018-04-25 16:00:00+00:00      329.653748              0.95   \n",
+       "9  2018-04-26 10:00:00+00:00      160.995972              0.95   \n",
+       "\n",
+       "   prediction_interval_lower_bound  prediction_interval_upper_bound  \\\n",
+       "0                        96.837778                       156.200644   \n",
+       "1                        -7.690994                       172.223388   \n",
+       "2                        78.019585                       182.094948   \n",
+       "3                       -16.565634                       111.036063   \n",
+       "4                       -61.080531                        62.602809   \n",
+       "5                        80.767928                       240.106157   \n",
+       "6                       207.344246                       435.492729   \n",
+       "7                       198.550187                       370.730941   \n",
+       "8                       201.918472                       457.389023   \n",
+       "9                        67.706721                       254.285223   \n",
+       "\n",
+       "  ai_forecast_status  \n",
+       "0                     \n",
+       "1                     \n",
+       "2                     \n",
+       "3                     \n",
+       "4                     \n",
+       "5                     \n",
+       "6                     \n",
+       "7                     \n",
+       "8                     \n",
+       "9                     \n",
+       "...\n",
+       "\n",
+       "[168 rows x 6 columns]"
       ]
      },
      "execution_count": 3,
@@ -183,7 +319,7 @@
      "data": {
       "text/html": [
        "\n",
-       "    Query processed 1.8 MB in 47 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:36efa98e-2843-4bc9-8225-06875236ef17&page=queryresults\">Job bigframes-dev:US.36efa98e-2843-4bc9-8225-06875236ef17 details</a>]\n",
+       "    Query processed 1.8 MB in 46 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:ac354d97-dc91-4d01-9dca-7069db6a26a7&page=queryresults\">Job bigframes-dev:US.ac354d97-dc91-4d01-9dca-7069db6a26a7 details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -197,7 +333,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 92.2 kB in a moment of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:18805b62-a8e2-4c69-a5bf-97aa19df8095&page=queryresults\">Job bigframes-dev:US.18805b62-a8e2-4c69-a5bf-97aa19df8095 details</a>]\n",
+       "    Query processed 92.2 kB in a moment of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:e61f41af-8761-4853-ae41-d38760c966ed&page=queryresults\">Job bigframes-dev:US.e61f41af-8761-4853-ae41-d38760c966ed details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -225,21 +361,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 10.8 kB in 11 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:a2b15286-6c7f-40a7-8009-045e5e4f3dbf&page=queryresults\">Job bigframes-dev:US.a2b15286-6c7f-40a7-8009-045e5e4f3dbf details</a>]\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "✅ Completed. \n",
-       "    Query processed 12.1 kB in a moment of slot time.\n",
+       "    Query processed 10.8 kB in a moment of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -266,22 +388,206 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b3816017ab4440c7bcf258df4a0ceff8",
+       "model_id": "0624fdda2be74b13bc6e6c30e38842b6",
        "version_major": 2,
        "version_minor": 1
       },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>forecast_timestamp</th>\n",
+       "      <th>forecast_value</th>\n",
+       "      <th>standard_error</th>\n",
+       "      <th>confidence_level</th>\n",
+       "      <th>prediction_interval_lower_bound</th>\n",
+       "      <th>prediction_interval_upper_bound</th>\n",
+       "      <th>confidence_interval_lower_bound</th>\n",
+       "      <th>confidence_interval_upper_bound</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-04-24 00:00:00+00:00</td>\n",
+       "      <td>52.768335</td>\n",
+       "      <td>34.87452</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-15.462203</td>\n",
+       "      <td>120.998872</td>\n",
+       "      <td>-15.462203</td>\n",
+       "      <td>120.998872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2018-04-24 01:00:00+00:00</td>\n",
+       "      <td>67.3281</td>\n",
+       "      <td>48.075255</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-26.729122</td>\n",
+       "      <td>161.385322</td>\n",
+       "      <td>-26.729122</td>\n",
+       "      <td>161.385322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2018-04-24 02:00:00+00:00</td>\n",
+       "      <td>75.205573</td>\n",
+       "      <td>53.910921</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-30.268884</td>\n",
+       "      <td>180.68003</td>\n",
+       "      <td>-30.268884</td>\n",
+       "      <td>180.68003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2018-04-24 03:00:00+00:00</td>\n",
+       "      <td>80.070922</td>\n",
+       "      <td>55.994076</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-29.479141</td>\n",
+       "      <td>189.620985</td>\n",
+       "      <td>-29.479141</td>\n",
+       "      <td>189.620985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2018-04-24 04:00:00+00:00</td>\n",
+       "      <td>75.161779</td>\n",
+       "      <td>56.583974</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-35.542394</td>\n",
+       "      <td>185.865952</td>\n",
+       "      <td>-35.542394</td>\n",
+       "      <td>185.865952</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2018-04-24 05:00:00+00:00</td>\n",
+       "      <td>81.428432</td>\n",
+       "      <td>56.85087</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-29.797913</td>\n",
+       "      <td>192.654778</td>\n",
+       "      <td>-29.797913</td>\n",
+       "      <td>192.654778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2018-04-24 06:00:00+00:00</td>\n",
+       "      <td>116.981445</td>\n",
+       "      <td>57.180767</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>5.109671</td>\n",
+       "      <td>228.853218</td>\n",
+       "      <td>5.109671</td>\n",
+       "      <td>228.853218</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2018-04-24 07:00:00+00:00</td>\n",
+       "      <td>237.222361</td>\n",
+       "      <td>57.770307</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>124.197176</td>\n",
+       "      <td>350.247546</td>\n",
+       "      <td>124.197176</td>\n",
+       "      <td>350.247546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2018-04-24 08:00:00+00:00</td>\n",
+       "      <td>323.722572</td>\n",
+       "      <td>58.681662</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>208.91436</td>\n",
+       "      <td>438.530784</td>\n",
+       "      <td>208.91436</td>\n",
+       "      <td>438.530784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2018-04-24 09:00:00+00:00</td>\n",
+       "      <td>357.288952</td>\n",
+       "      <td>59.806906</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>240.279247</td>\n",
+       "      <td>474.298656</td>\n",
+       "      <td>240.279247</td>\n",
+       "      <td>474.298656</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 8 columns</p>\n",
+       "</div>[168 rows x 8 columns in total]"
+      ],
       "text/plain": [
-       "<bigframes.display.anywidget.TableWidget object at 0x7efcec227c50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "Computation deferred. Computation will process 10.8 kB"
+       "          forecast_timestamp  forecast_value  standard_error  \\\n",
+       "0  2018-04-24 00:00:00+00:00       52.768335        34.87452   \n",
+       "1  2018-04-24 01:00:00+00:00         67.3281       48.075255   \n",
+       "2  2018-04-24 02:00:00+00:00       75.205573       53.910921   \n",
+       "3  2018-04-24 03:00:00+00:00       80.070922       55.994076   \n",
+       "4  2018-04-24 04:00:00+00:00       75.161779       56.583974   \n",
+       "5  2018-04-24 05:00:00+00:00       81.428432        56.85087   \n",
+       "6  2018-04-24 06:00:00+00:00      116.981445       57.180767   \n",
+       "7  2018-04-24 07:00:00+00:00      237.222361       57.770307   \n",
+       "8  2018-04-24 08:00:00+00:00      323.722572       58.681662   \n",
+       "9  2018-04-24 09:00:00+00:00      357.288952       59.806906   \n",
+       "\n",
+       "   confidence_level  prediction_interval_lower_bound  \\\n",
+       "0              0.95                       -15.462203   \n",
+       "1              0.95                       -26.729122   \n",
+       "2              0.95                       -30.268884   \n",
+       "3              0.95                       -29.479141   \n",
+       "4              0.95                       -35.542394   \n",
+       "5              0.95                       -29.797913   \n",
+       "6              0.95                         5.109671   \n",
+       "7              0.95                       124.197176   \n",
+       "8              0.95                        208.91436   \n",
+       "9              0.95                       240.279247   \n",
+       "\n",
+       "   prediction_interval_upper_bound  confidence_interval_lower_bound  \\\n",
+       "0                       120.998872                       -15.462203   \n",
+       "1                       161.385322                       -26.729122   \n",
+       "2                        180.68003                       -30.268884   \n",
+       "3                       189.620985                       -29.479141   \n",
+       "4                       185.865952                       -35.542394   \n",
+       "5                       192.654778                       -29.797913   \n",
+       "6                       228.853218                         5.109671   \n",
+       "7                       350.247546                       124.197176   \n",
+       "8                       438.530784                        208.91436   \n",
+       "9                       474.298656                       240.279247   \n",
+       "\n",
+       "   confidence_interval_upper_bound  \n",
+       "0                       120.998872  \n",
+       "1                       161.385322  \n",
+       "2                        180.68003  \n",
+       "3                       189.620985  \n",
+       "4                       185.865952  \n",
+       "5                       192.654778  \n",
+       "6                       228.853218  \n",
+       "7                       350.247546  \n",
+       "8                       438.530784  \n",
+       "9                       474.298656  \n",
+       "...\n",
+       "\n",
+       "[168 rows x 8 columns]"
       ]
      },
      "execution_count": 4,
@@ -324,7 +630,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 31.7 MB in 10 seconds of slot time.\n",
+       "    Query processed 31.7 MB in 11 seconds of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -338,7 +644,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 58.8 MB in 7 seconds of slot time.\n",
+       "    Query processed 58.8 MB in 12 seconds of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -360,7 +666,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -402,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "6dbe6c48",
    "metadata": {},
    "outputs": [
@@ -410,20 +716,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/core/log_adapter.py:182: TimeTravelCacheWarning: Reading cached table from 2025-12-12 03:47:11.144938+00:00 to avoid\n",
+      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/core/log_adapter.py:182: TimeTravelCacheWarning: Reading cached table from 2025-12-12 23:04:48.874384+00:00 to avoid\n",
       "incompatibilies with previous reads of this table. To read the latest\n",
       "version, set `use_cache=False` or close the current session with\n",
       "Session.close() or bigframes.pandas.close_session().\n",
-      "  return method(*args, **kwargs)\n",
-      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/ml/forecasting.py:238: UserWarning: Converting Date column 'date' to datetime for hourly frequency. This is required because BigQuery ML doesn't support Date type with hourly frequency.\n",
-      "  warnings.warn(\n"
+      "  return method(*args, **kwargs)\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "\n",
-       "    Query processed 39.4 MB in 2 hours of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:16d735c6-c885-447f-b513-5249ee8cb48a&page=queryresults\">Job bigframes-dev:US.16d735c6-c885-447f-b513-5249ee8cb48a details</a>]\n",
+       "✅ Completed. \n",
+       "    Query processed 69.8 MB in a moment of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -433,11 +737,18 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of stations: 41\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 32.0 MB in 3 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:c4cf8019-bdf6-461a-8753-e7314b837c29&page=queryresults\">Job bigframes-dev:US.c4cf8019-bdf6-461a-8753-e7314b837c29 details</a>]\n",
+       "    Query processed 69.8 MB in a moment of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -451,7 +762,28 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 4.6 kB in a moment of slot time.\n",
+       "    Query processed 69.8 MB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date range: 2013-08-29 to 2018-04-30\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    Query processed 18.8 MB in 2 minutes of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:74ada07a-98ad-4d03-90bb-2b98f1d8b558&page=queryresults\">Job bigframes-dev:US.74ada07a-98ad-4d03-90bb-2b98f1d8b558 details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -465,7 +797,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 11.5 kB in 8 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:13663bcd-b3e2-471c-b7e7-50c260c4cfdd&page=queryresults\">Job bigframes-dev:US.13663bcd-b3e2-471c-b7e7-50c260c4cfdd details</a>]\n",
+       "    Query processed 1.4 MB in 4 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:a292f715-1d9c-406d-a7d5-f99b2ba71660&page=queryresults\">Job bigframes-dev:US.a292f715-1d9c-406d-a7d5-f99b2ba71660 details</a>]\n",
        "    "
       ],
       "text/plain": [
@@ -479,7 +811,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 12.4 kB in a moment of slot time.\n",
+       "    Query processed 4.6 kB in a moment of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -493,7 +825,7 @@
      "data": {
       "text/html": [
        "✅ Completed. \n",
-       "    Query processed 0 Bytes in a moment of slot time.\n",
+       "    Query processed 11.5 kB in a moment of slot time.\n",
        "    "
       ],
       "text/plain": [
@@ -505,13 +837,13 @@
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5cd08895656f45f5bfe39fd6bb26855f",
-       "version_major": 2,
-       "version_minor": 1
-      },
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 0 Bytes in a moment of slot time.\n",
+       "    "
+      ],
       "text/plain": [
-       "<bigframes.display.anywidget.TableWidget object at 0x7efcec160550>"
+       "<IPython.core.display.HTML object>"
       ]
      },
      "metadata": {},
@@ -519,12 +851,221 @@
     },
     {
      "data": {
-      "text/html": [],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00fc1edbf6fd40dfb949a3e3a30b6c3e",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>forecast_timestamp</th>\n",
+       "      <th>start_station_name</th>\n",
+       "      <th>forecast_value</th>\n",
+       "      <th>standard_error</th>\n",
+       "      <th>confidence_level</th>\n",
+       "      <th>prediction_interval_lower_bound</th>\n",
+       "      <th>prediction_interval_upper_bound</th>\n",
+       "      <th>confidence_interval_lower_bound</th>\n",
+       "      <th>confidence_interval_upper_bound</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Beale at Market</td>\n",
+       "      <td>27.911417</td>\n",
+       "      <td>3.422434</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>21.215568</td>\n",
+       "      <td>34.607265</td>\n",
+       "      <td>21.215568</td>\n",
+       "      <td>34.607265</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Civic Center BART (7th at Market)</td>\n",
+       "      <td>17.09455</td>\n",
+       "      <td>4.266287</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>8.74774</td>\n",
+       "      <td>25.441361</td>\n",
+       "      <td>8.74774</td>\n",
+       "      <td>25.441361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Embarcadero at Bryant</td>\n",
+       "      <td>22.343648</td>\n",
+       "      <td>3.393702</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>15.704012</td>\n",
+       "      <td>28.983284</td>\n",
+       "      <td>15.704012</td>\n",
+       "      <td>28.983284</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Embarcadero at Folsom</td>\n",
+       "      <td>28.25329</td>\n",
+       "      <td>3.382158</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>21.63624</td>\n",
+       "      <td>34.870339</td>\n",
+       "      <td>21.63624</td>\n",
+       "      <td>34.870339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Embarcadero at Sansome</td>\n",
+       "      <td>52.538083</td>\n",
+       "      <td>6.269291</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>40.272477</td>\n",
+       "      <td>64.803689</td>\n",
+       "      <td>40.272477</td>\n",
+       "      <td>64.803689</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Embarcadero at Vallejo</td>\n",
+       "      <td>16.513233</td>\n",
+       "      <td>2.953683</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>10.734476</td>\n",
+       "      <td>22.29199</td>\n",
+       "      <td>10.734476</td>\n",
+       "      <td>22.29199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Market at 10th</td>\n",
+       "      <td>34.051274</td>\n",
+       "      <td>6.205798</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>21.90989</td>\n",
+       "      <td>46.192658</td>\n",
+       "      <td>21.90989</td>\n",
+       "      <td>46.192658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Market at 4th</td>\n",
+       "      <td>25.746029</td>\n",
+       "      <td>4.001592</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>17.917082</td>\n",
+       "      <td>33.574977</td>\n",
+       "      <td>17.917082</td>\n",
+       "      <td>33.574977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Market at Sansome</td>\n",
+       "      <td>46.134368</td>\n",
+       "      <td>5.071852</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>36.211503</td>\n",
+       "      <td>56.057233</td>\n",
+       "      <td>36.211503</td>\n",
+       "      <td>56.057233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Mechanics Plaza (Market at Battery)</td>\n",
+       "      <td>23.269941</td>\n",
+       "      <td>3.194675</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>17.019692</td>\n",
+       "      <td>29.520189</td>\n",
+       "      <td>17.019692</td>\n",
+       "      <td>29.520189</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 9 columns</p>\n",
+       "</div>[123 rows x 9 columns in total]"
+      ],
       "text/plain": [
-       "Computation deferred. Computation will process 11.5 kB"
+       "          forecast_timestamp                   start_station_name  \\\n",
+       "0  2016-09-01 00:00:00+00:00                      Beale at Market   \n",
+       "1  2016-09-01 00:00:00+00:00    Civic Center BART (7th at Market)   \n",
+       "2  2016-09-01 00:00:00+00:00                Embarcadero at Bryant   \n",
+       "3  2016-09-01 00:00:00+00:00                Embarcadero at Folsom   \n",
+       "4  2016-09-01 00:00:00+00:00               Embarcadero at Sansome   \n",
+       "5  2016-09-01 00:00:00+00:00               Embarcadero at Vallejo   \n",
+       "6  2016-09-01 00:00:00+00:00                       Market at 10th   \n",
+       "7  2016-09-01 00:00:00+00:00                        Market at 4th   \n",
+       "8  2016-09-01 00:00:00+00:00                    Market at Sansome   \n",
+       "9  2016-09-01 00:00:00+00:00  Mechanics Plaza (Market at Battery)   \n",
+       "\n",
+       "   forecast_value  standard_error  confidence_level  \\\n",
+       "0       27.911417        3.422434              0.95   \n",
+       "1        17.09455        4.266287              0.95   \n",
+       "2       22.343648        3.393702              0.95   \n",
+       "3        28.25329        3.382158              0.95   \n",
+       "4       52.538083        6.269291              0.95   \n",
+       "5       16.513233        2.953683              0.95   \n",
+       "6       34.051274        6.205798              0.95   \n",
+       "7       25.746029        4.001592              0.95   \n",
+       "8       46.134368        5.071852              0.95   \n",
+       "9       23.269941        3.194675              0.95   \n",
+       "\n",
+       "   prediction_interval_lower_bound  prediction_interval_upper_bound  \\\n",
+       "0                        21.215568                        34.607265   \n",
+       "1                          8.74774                        25.441361   \n",
+       "2                        15.704012                        28.983284   \n",
+       "3                         21.63624                        34.870339   \n",
+       "4                        40.272477                        64.803689   \n",
+       "5                        10.734476                         22.29199   \n",
+       "6                         21.90989                        46.192658   \n",
+       "7                        17.917082                        33.574977   \n",
+       "8                        36.211503                        56.057233   \n",
+       "9                        17.019692                        29.520189   \n",
+       "\n",
+       "   confidence_interval_lower_bound  confidence_interval_upper_bound  \n",
+       "0                        21.215568                        34.607265  \n",
+       "1                          8.74774                        25.441361  \n",
+       "2                        15.704012                        28.983284  \n",
+       "3                         21.63624                        34.870339  \n",
+       "4                        40.272477                        64.803689  \n",
+       "5                        10.734476                         22.29199  \n",
+       "6                         21.90989                        46.192658  \n",
+       "7                        17.917082                        33.574977  \n",
+       "8                        36.211503                        56.057233  \n",
+       "9                        17.019692                        29.520189  \n",
+       "...\n",
+       "\n",
+       "[123 rows x 9 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -532,19 +1073,39 @@
    "source": [
     "df_multi = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n",
     "df_multi = df_multi[df_multi[\"start_station_name\"].str.contains(\"Market|Powell|Embarcadero\")]\n",
+    "  \n",
+    "# Create daily aggregation\n",
     "features = bpd.DataFrame({\n",
     "    \"start_station_name\": df_multi[\"start_station_name\"],\n",
-    "    \"num_trips\": df_multi[\"start_date\"],\n",
     "    \"date\": df_multi[\"start_date\"].dt.date,\n",
     "})\n",
+    "\n",
+    "# Group by station and date\n",
     "num_trips = features.groupby(\n",
     "    [\"start_station_name\", \"date\"], as_index=False\n",
-    ").count()\n",
+    ").size()\n",
+    "# Rename the size column to \"num_trips\"\n",
+    "num_trips = num_trips.rename(columns={num_trips.columns[-1]: \"num_trips\"})\n",
+    "\n",
+    "# Check data quality\n",
+    "print(f\"Number of stations: {num_trips['start_station_name'].nunique()}\")\n",
+    "print(f\"Date range: {num_trips['date'].min()} to {num_trips['date'].max()}\")\n",
+    "\n",
+    "# Use daily frequency  \n",
+    "model = forecasting.ARIMAPlus(\n",
+    "    data_frequency=\"daily\",\n",
+    "    horizon=30,\n",
+    "    auto_arima_max_order=3,\n",
+    "    min_time_series_length=10,\n",
+    "    time_series_length_fraction=0.8\n",
+    ")\n",
+    "\n",
     "model.fit(\n",
     "    num_trips[[\"date\"]],\n",
     "    num_trips[[\"num_trips\"]],\n",
     "    id_col=num_trips[[\"start_station_name\"]]\n",
     ")\n",
+    "\n",
     "predictions_multi = model.predict()\n",
     "predictions_multi"
    ]
diff --git a/tests/system/large/ml/test_forecasting.py b/tests/system/large/ml/test_forecasting.py
index 789068001f..72a0ee469b 100644
--- a/tests/system/large/ml/test_forecasting.py
+++ b/tests/system/large/ml/test_forecasting.py
@@ -17,7 +17,7 @@
 from bigframes.ml import forecasting
 from bigframes.testing import utils
 
-ARIMA_EVALUATE_OUTPUT_COLUMNS = [
+ARIMA_EVALUATE_OUTPUT_COL = [
     "non_seasonal_p",
     "non_seasonal_d",
     "non_seasonal_q",
@@ -106,9 +106,9 @@ def test_arima_plus_model_fit_summary(
     curr_model = arima_model_w_id if id_col_name else arima_model
     result = curr_model.summary().to_pandas()
     expected_columns = (
-        [id_col_name] + ARIMA_EVALUATE_OUTPUT_COLUMNS
+        [id_col_name] + ARIMA_EVALUATE_OUTPUT_COL
         if id_col_name
-        else ARIMA_EVALUATE_OUTPUT_COLUMNS
+        else ARIMA_EVALUATE_OUTPUT_COL
     )
     utils.check_pandas_df_schema_and_index(
         result, columns=expected_columns, index=2 if id_col_name else 1
@@ -190,24 +190,3 @@ def test_arima_plus_model_fit_params(
     assert reloaded_model.min_time_series_length == 10
     assert reloaded_model.trend_smoothing_window_size == 5
     assert reloaded_model.decompose_time_series is False
-
-
-def test_arima_plus_model_fit_date_conversion(time_series_df_default_index):
-    model = forecasting.ARIMAPlus(data_frequency="hourly")
-
-    # Arrange: Create a dataframe with a date column to test auto-conversion
-    df = time_series_df_default_index.copy()
-    df["parsed_date"] = df["parsed_date"].dt.date
-
-    X_train = df[["parsed_date"]]
-    y_train = df[["total_visits"]]
-
-    with pytest.warns(
-        UserWarning,
-        match="Converting Date column 'parsed_date' to datetime for hourly frequency.",
-    ):
-        # Act
-        model.fit(X_train, y_train)
-
-    # Assert
-    assert model._bqml_model is not None