docs: add python code sample to multiple timeseries forecasting

samples/snippets/create_multiple_timeseries_forecasting_model.py

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+# 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]