docs: add python code sample fore multiple forecasting time series (#531)

* docs: add python code sample to multiple timeseries forecasting

---------

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

Author: DevStephanie

samples/snippets/create_multiple_timeseries_forecasting_model.py

@@ -0,0 +1,98 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# 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 writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+def test_multiple_timeseries_forecasting_model(random_model_id):
+    your_model_id = random_model_id
+
+    # [START bigquery_dataframes_bqml_arima_multiple_step_2_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,
+        }
+    )
+    date = df["starttime"].dt.date
+    df.groupby([date])
+    num_trips = features.groupby(["date"]).count()
+
+    # Results from running "print(num_trips)"
+
+    #                num_trips
+    # date
+    # 2013-07-01      16650
+    # 2013-07-02      22745
+    # 2013-07-03      21864
+    # 2013-07-04      22326
+    # 2013-07-05      21842
+    # 2013-07-06      20467
+    # 2013-07-07      20477
+    # 2013-07-08      21615
+    # 2013-07-09      26641
+    # 2013-07-10      25732
+    # 2013-07-11      24417
+    # 2013-07-12      19006
+    # 2013-07-13      26119
+    # 2013-07-14      29287
+    # 2013-07-15      28069
+    # 2013-07-16      29842
+    # 2013-07-17      30550
+    # 2013-07-18      28869
+    # 2013-07-19      26591
+    # 2013-07-20      25278
+    # 2013-07-21      30297
+    # 2013-07-22      25979
+    # 2013-07-23      32376
+    # 2013-07-24      35271
+    # 2013-07-25      31084
+
+    num_trips.plot.line(
+        # Rotate the x labels so they are more visible.
+        rot=45,
+    )
+
+    # [END bigquery_dataframes_bqml_arima_multiple_step_2_visualize]
+
+    # [START bigquery_dataframes_bqml_arima_multiple_step_3_fit]
+    from bigframes.ml import forecasting
+    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"], as_index=False).count()
+    model = forecasting.ARIMAPlus()
+
+    X = num_trips["date"].to_frame()
+    y = num_trips["num_trips"].to_frame()
+
+    model.fit(X, y)
+    # The model.fit() call above created a temporary model.
+    # Use the to_gbq() method to write to a permanent location.
+
+    model.to_gbq(
+        your_model_id,  # For example: "bqml_tutorial.nyc_citibike_arima_model",
+        replace=True,
+    )
+    # [END bigquery_dataframes_bqml_arima_multiple_step_3_fit]