docs: add snippets for visualizing a time series and creating a time series model for the Limit forecasted values in time series model tutorial (#1310)

* merge main

* temp snippet

* fix typo

* Delete demo.ipynb

* Update samples/snippets/limit_single_timeseries_forecasting_model_test.py

Co-authored-by: Tim Sweña (Swast) <[email protected]>

* Update samples/snippets/limit_single_timeseries_forecasting_model_test.py

Co-authored-by: Tim Sweña (Swast) <[email protected]>

* Update samples/snippets/limit_single_timeseries_forecasting_model_test.py

Co-authored-by: Tim Sweña (Swast) <[email protected]>

* Update samples/snippets/limit_single_timeseries_forecasting_model_test.py

Co-authored-by: Tim Sweña (Swast) <[email protected]>

* Update samples/snippets/limit_single_timeseries_forecasting_model_test.py

Co-authored-by: Tim Sweña (Swast) <[email protected]>

* add plot line for visualization

* change bar chart to line chart

---------

Co-authored-by: Tim Sweña (Swast) <[email protected]>

Author: rey-esp

samples/snippets/limit_single_timeseries_forecasting_model_test.py

@@ -0,0 +1,64 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (t
+# you may not use this file except in compliance wi
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in
+# distributed under the License is distributed on a
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, eit
+# See the License for the specific language governi
+# limitations under the License.
+
+
+def test_limit_single_timeseries(random_model_id: str) -> None:
+    your_model_id = random_model_id
+
+    # [START bigquery_dataframes_bqml_limit_forecast_visualize]
+    import bigframes.pandas as bpd
+
+    df = bpd.read_gbq("bigquery-public-data.new_york.citibike_trips")
+
+    features = bpd.DataFrame(
+        {
+            "num_trips": df.starttime,
+            "date": df["starttime"].dt.date,
+        }
+    )
+    num_trips = features.groupby(["date"]).count()
+
+    num_trips.plot.line()
+    # [END bigquery_dataframes_bqml_limit_forecast_visualize]
+
+    # [START bigquery_dataframes_bqml_limit_forecast_create]
+    from bigframes.ml import forecasting
+    import bigframes.pandas as bpd
+
+    df = bpd.read_gbq("bigquery-public-data.new_york.citibike_trips")
+
+    features = bpd.DataFrame(
+        {
+            "start_station_id": df["start_station_id"],
+            "num_trips": df.starttime,
+            "date": df["starttime"].dt.date,
+        }
+    )
+    num_trips = features.groupby(["date", "start_station_id"], as_index=False).count()
+    model = forecasting.ARIMAPlus()
+
+    X = num_trips[["date"]]
+    y = num_trips[["num_trips"]]
+    id_col = num_trips[["start_station_id"]]
+
+    model.fit(X, y, id_col=id_col)
+
+    model.to_gbq(
+        your_model_id,  # For example: "bqml_tutorial.nyc_citibike_arima_model",
+        replace=True,
+    )
+    # [END bigquery_dataframes_bqml_limit_forecast_create]
+    assert df is not None
+    assert features is not None
+    assert num_trips is not None