googleapis · SalemJorden · Jan 29, 2024 · Jan 29, 2024 · Feb 20, 2024 · Feb 29, 2024
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+# Copyright 2023 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_create_single_timeseries(random_model_id):
+    your_model_id = random_model_id
+
+    # [START bigquery_dataframes_single_timeseries_forecasting_model_tutorial]
+    import bigframes.pandas as bpd
+
+    # Start by selecting the data you'll use for training. `read_gbq` accepts
+    # either a SQL query or a table ID. Since this example selects from multiple
+    # tables via a wildcard, use SQL to define this data. Watch issue
+    # https://github.com/googleapis/python-bigquery-dataframes/issues/169
+    # for updates to `read_gbq` to support wildcard tables.
+
+    # Read and visualize the time series you want to forecast.
+    df = bpd.read_gbq(
+        'bigquery-public-data.google_analytics_sample.ga_sessions_*'
+        )
+    parsed_date = bpd.to_datetime(df.date, format= "%Y%m%d", utc = True)
+    total_visits = df.groupby(["date"])["parsed_date"].sum()
+    visits = df["totals"].struct.field("visits")
+
+    # Create an Arima-based time series model using the Google Analytics 360 data. 
+    from bigframes.ml.forecasting import ARIMAPlus
+
+    ga_arima_model = ARIMAPlus()
+
+    X = df[["parsed_date"]]
+    y = df[["total_visits"]]
+
+    # Fit the model to your dataframe.
+    ga_arima_model.fit(X,y)
+
+    # The model.fit() call above created a temporary model.
+    # Use the to_gbq() method to write to a permanent location.
+    ga_arima_model.to_gbq(
+    your_model_id,  # For example: "bqml_tutorial.sample_model",
+    replace=True,
+    )
+
+    # Inspect the evaluation metrics of all evaluated models.
+    # when running this function use same model, dataset, model name (str)
+    evaluation = ga_arima_model.summary(
+        show_all_candidate_models = False,
+        )
+
+    print(evaluation)
+
+    # Inspect the coefficients of your model
+    f'''
+    SELECT *
+    FROM ML.ARIMA_COEFFICIENTS(MODEL `{your_model_id}`)
+    '''
+    evaluation.ML.ARIMA_COEFFICIENTS()
+
+    # Use your model to forecast the time series
+    #standardSQL
+    your_model_id.forecast()
+
+    # Explain and visualize the forecasting results
+    f'''
+    SELECT *
+    FROM ML.EXPLAIN_FORECAST(
+    MODEL `{your_model_id}`,
+    STRUCT(
+    [horizon AS horizon]
+    [, confidence_level AS confidence_level]))
+    '''