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    Forecast a single time series with an ARIMA_PLUS univariate model

    This tutorial teaches you how to use an ARIMA_PLUS univariate time series model to forecast the future value for a given column based on the historical values for that column.

    This tutorial forecasts a single time series. Forecasted values are calculated once for each time point in the input data.

    This tutorial uses data from the public bigquery-public-data.google_analytics_sample.ga_sessions sample table . This table contains obfuscated ecommerce data from the Google Merchandise Store.

    Objectives

    This tutorial guides you through completing the following tasks:

    Costs

    This tutorial uses billable components of Google Cloud, including the following:

    • BigQuery
    • BigQuery ML

    For more information about BigQuery costs, see the BigQuery pricing page.

    For more information about BigQuery ML costs, see BigQuery ML pricing .

    Before you begin

    1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

    2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

      Roles required to select or create a project

      • Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
      • Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

      Go to project selector

    3. Verify that billing is enabled for your Google Cloud project .

    4. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

      Roles required to select or create a project

      • Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
      • Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

      Go to project selector

    5. Verify that billing is enabled for your Google Cloud project .

      6.
    6. BigQuery is automatically enabled in new projects. To activate BigQuery in a pre-existing project, go to

      Enable the BigQuery API.

      Roles required to enable APIs

      To enable APIs, you need the Service Usage Admin IAM role ( roles/serviceusage.serviceUsageAdmin ), which contains the serviceusage.services.enable permission. Learn how to grant roles .

      Enable the API

    Required Permissions

    • To create the dataset, you need the bigquery.datasets.create IAM permission.

    • To create the model, you need the following permissions:

      • bigquery.jobs.create
      • bigquery.models.create
      • bigquery.models.getData
      • bigquery.models.updateData

    • To run inference, you need the following permissions:

      • bigquery.models.getData
      • bigquery.jobs.create

    For more information about IAM roles and permissions in BigQuery, see Introduction to IAM .

    Create a dataset

    Create a BigQuery dataset to store your ML model.

    Console

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to the BigQuery page

    2. In the Explorerpane, click your project name.

    3. Click View actions > Create dataset

    4. On the Create datasetpage, do the following:

      • For Dataset ID, enter bqml_tutorial .

      • For Location type, select Multi-region, and then select US.

      • Leave the remaining default settings as they are, and click Create dataset.

    bq

    To create a new dataset, use the bq mk --dataset command .

    1. Create a dataset named bqml_tutorial with the data location set to US .

      bq mk --dataset \
  --location=US \
  --description "BigQuery ML tutorial dataset." \
  bqml_tutorial

    2. Confirm that the dataset was created:

      bq  
ls

    API

    Call the datasets.insert method with a defined dataset resource .

     { 
  
 "datasetReference" 
 : 
  
 { 
  
 "datasetId" 
 : 
  
 "bqml_tutorial" 
  
 } 
 }

    BigQuery DataFrames

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      import 
  
 google.cloud.bigquery 
 bqclient 
 = 
 google 
 . 
 cloud 
 . 
  bigquery 
 
 . 
  Client 
 
 () 
 bqclient 
 . 
  create_dataset 
 
 ( 
 "bqml_tutorial" 
 , 
 exists_ok 
 = 
 True 
 )

    Visualize the input data

    Before creating the model, you can optionally visualize your input time series data to get a sense of the distribution. You can do this by using Data Studio.

    Follow these steps to visualize the time series data:

    SQL

    In the following GoogleSQL query, the SELECT statement parses the date column from the input table to the TIMESTAMP type and renames it to parsed_date , and uses the SUM(...) clause and the GROUP BY date clause to create a daily totals.visits value.

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, paste in the following query and click Run:

       SELECT 
 PARSE_TIMESTAMP 
 ( 
 "%Y%m%d" 
 , 
  
 date 
 ) 
  
 AS 
  
 parsed_date 
 , 
 SUM 
 ( 
 totals 
 . 
 visits 
 ) 
  
 AS 
  
 total_visits 
 FROM 
 `bigquery-public-data.google_analytics_sample.ga_sessions_*` 
 GROUP 
  
 BY 
  
 date 
 ;

      1. When the query completes, click Open in > Data Studio. Data Studio opens in a new tab. Complete the following steps in the new tab.

      2. In Data Studio, click Insert > Time series chart.

      3. In the Chartpane, choose the Setuptab.

      4. In the Metricsection, add the total_visitsfield, and remove the default Record Countmetric. The resulting chart looks similar to the following:

        Result_visualization

        Looking at the chart, you can see that the input time series has a weekly seasonal pattern.

    BigQuery DataFrames

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      import 
  
 bigframes.pandas 
  
 as 
  
 bpd 
 # Start by loading the historical data from BigQuerythat you want to analyze and forecast. 
 # This clause indicates that you are querying the ga_sessions_* tables in the google_analytics_sample dataset. 
 # 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 
 ) 
 parsed_date 
 . 
 name 
 = 
 "parsed_date" 
 visits 
 = 
 df 
 [ 
 "totals" 
 ] 
 . 
 struct 
 . 
 field 
 ( 
 "visits" 
 ) 
 visits 
 . 
 name 
 = 
 "total_visits" 
 total_visits 
 = 
 visits 
 . 
 groupby 
 ( 
 parsed_date 
 ) 
 . 
 sum 
 () 
 # Expected output: total_visits.head() 
 # parsed_date 
 # 2016-08-01 00:00:00+00:00    1711 
 # 2016-08-02 00:00:00+00:00    2140 
 # 2016-08-03 00:00:00+00:00    2890 
 # 2016-08-04 00:00:00+00:00    3161 
 # 2016-08-05 00:00:00+00:00    2702 
 # Name: total_visits, dtype: Int64 
 total_visits 
 . 
 plot 
 . 
 line 
 ()

    The result is similar to the following:Result_visualization

    Create the time series model

    Create a time series model to forecast total site visits as represented by totals.visits column, and train it on the Google Analytics 360 data.

    SQL

    In the following query, the OPTIONS(model_type='ARIMA_PLUS', time_series_timestamp_col='date', ...) clause indicates that you are creating an ARIMA -based time series model. The auto_arima option of the CREATE MODEL statement defaults to TRUE , so the auto.ARIMA algorithm automatically tunes the hyperparameters in the model. The algorithm fits dozens of candidate models and chooses the best model, which is the model with the lowest Akaike information criterion (AIC) . The data_frequency option of the CREATE MODEL statements defaults to AUTO_FREQUENCY , so the training process automatically infers the data frequency of the input time series. The decompose_time_series option of the CREATE MODEL statement defaults to TRUE , so that information about the time series data is returned when you evaluate the model in the next step.

    Follow these steps to create the model:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, paste in the following query and click Run:

       CREATE 
  
 OR 
  
 REPLACE 
  
 MODEL 
  
 `bqml_tutorial.ga_arima_model` 
 OPTIONS 
 ( 
 model_type 
  
 = 
  
 'ARIMA_PLUS' 
 , 
  
 time_series_timestamp_col 
  
 = 
  
 'parsed_date' 
 , 
  
 time_series_data_col 
  
 = 
  
 'total_visits' 
 , 
  
 auto_arima 
  
 = 
  
 TRUE 
 , 
  
 data_frequency 
  
 = 
  
 'AUTO_FREQUENCY' 
 , 
  
 decompose_time_series 
  
 = 
  
 TRUE 
 ) 
  
 AS 
 SELECT 
 PARSE_TIMESTAMP 
 ( 
 "%Y%m%d" 
 , 
  
 date 
 ) 
  
 AS 
  
 parsed_date 
 , 
 SUM 
 ( 
 totals 
 . 
 visits 
 ) 
  
 AS 
  
 total_visits 
 FROM 
 `bigquery-public-data.google_analytics_sample.ga_sessions_*` 
 GROUP 
  
 BY 
  
 date 
 ;

      The query takes about 4 seconds to complete, after which you can access the ga_arima_model model. Because the query uses a CREATE MODEL statement to create a model, you don't see query results.

    BigQuery DataFrames

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      from 
  
 bigframes.ml 
  
 import 
 forecasting 
 import 
  
 bigframes.pandas 
  
 as 
  
 bpd 
 # Create a time series model to forecast total site visits: 
 # The auto_arima option defaults to True, so the auto.ARIMA algorithm automatically 
 # tunes the hyperparameters in the model. 
 # The data_frequency option defaults to 'auto_frequency so the training 
 # process automatically infers the data frequency of the input time series. 
 # The decompose_time_series option defaults to True, so that information about 
 # the time series data is returned when you evaluate the model in the next step. 
 model 
 = 
 forecasting 
 . 
 ARIMAPlus 
 () 
 model 
 . 
 auto_arima 
 = 
 True 
 model 
 . 
 data_frequency 
 = 
 "auto_frequency" 
 model 
 . 
 decompose_time_series 
 = 
 True 
 # Use the data loaded in the previous step to fit the model 
 training_data 
 = 
 total_visits 
 . 
 to_frame 
 () 
 . 
 reset_index 
 ( 
 drop 
 = 
 False 
 ) 
 X 
 = 
 training_data 
 [[ 
 "parsed_date" 
 ]] 
 y 
 = 
 training_data 
 [[ 
 "total_visits" 
 ]] 
 model 
 . 
 fit 
 ( 
 X 
 , 
 y 
 )

    Evaluate the candidate models

    SQL

    Evaluate the time series models by using the ML.ARIMA_EVALUATE function. The ML.ARIMA_EVALUATE function shows you the evaluation metrics of all the candidate models evaluated during the process of automatic hyperparameter tuning.

    Follow these steps to evaluate the model:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, paste in the following query and click Run:

       SELECT 
 * 
 FROM 
 ML 
 . 
 ARIMA_EVALUATE 
 ( 
 MODEL 
  
 `bqml_tutorial.ga_arima_model` 
 );

      The results should look similar to the following:

      ML.ARIMA_EVALUATE output.

    BigQuery DataFrames

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      # Evaluate the time series models by using the summary() function. The summary() 
 # function shows you the evaluation metrics of all the candidate models evaluated 
 # during the process of automatic hyperparameter tuning. 
 summary 
 = 
 model 
 . 
 summary 
 ( 
 show_all_candidate_models 
 = 
 True 
 , 
 ) 
 print 
 ( 
 summary 
 . 
 peek 
 ()) 
 # Expected output: 
 # row   non_seasonal_p	non_seasonal_d	non_seasonal_q	has_drift	log_likelihood	AIC	variance	seasonal_periods	has_holiday_effect	has_spikes_and_dips	has_step_changes	error_message 
 #  0	      0	              1	               3	      True	     -2464.255656	4938.511313	     42772.506055	        ['WEEKLY']	            False	        False	            True 
 #  1	      2	              1	               0	      False	     -2473.141651	4952.283303	     44942.416463	        ['WEEKLY']	            False	        False	            True 
 #  2	      1	              1	               0 	      False	     -2479.880885	4963.76177	     46642.953433	        ['WEEKLY']	            False	        False	            True 
 #  3	      0	              1	               1	      False	     -2470.632377	4945.264753	     44319.379307	        ['WEEKLY']	            False	        False	            True 
 #  4	      2	              1	               1	      True	     -2463.671247	4937.342493	     42633.299513	        ['WEEKLY']	            False	        False	            True

    The non_seasonal_p , non_seasonal_d , non_seasonal_q , and has_drift output columns define an ARIMA model in the training pipeline. The log_likelihood , AIC , and variance output columns are relevant to the ARIMA model fitting process.

    The auto.ARIMA algorithm uses the KPSS test to determine the best value for non_seasonal_d , which in this case is 1 . When non_seasonal_d is 1 , the auto.ARIMA algorithm trains 42 different candidate ARIMA models in parallel. In this example, all 42 candidate models are valid, so the output contains 42 rows, one for each candidate ARIMA model; in cases where some of the models aren't valid, they are excluded from the output. These candidate models are returned in ascending order by AIC. The model in the first row has the lowest AIC, and is considered the best model. The best model is saved as the final model and is used when you call functions such as ML.FORECAST on the model

    The seasonal_periods column contains information about the seasonal pattern identified in the time series data. It has nothing to do with the ARIMA modeling, therefore it has the same value across all output rows. It reports a weekly pattern, which agrees with the results you saw if you chose to visualize the input data.

    The has_holiday_effect , has_spikes_and_dips , and has_step_changes columns are only populated when decompose_time_series=TRUE . These columns also reflect information about the input time series data, and are not related to the ARIMA modeling. These columns also have the same values across all output rows.

    The error_message column shows any errors that incurred during the auto.ARIMA fitting process. One possible reason for errors is when the selected non_seasonal_p , non_seasonal_d , non_seasonal_q , and has_drift columns are not able to stabilize the time series. To retrieve the error message of all the candidate models, set the show_all_candidate_models option to TRUE when you create the model.

    For more information about the output columns, see ML.ARIMA_EVALUATE function .

    Inspect the model's coefficients

    SQL

    Inspect the time series model's coefficients by using the ML.ARIMA_COEFFICIENTS function.

    Follow these steps to retrieve the model's coefficients:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, paste in the following query and click Run:

       SELECT 
 * 
 FROM 
 ML 
 . 
 ARIMA_COEFFICIENTS 
 ( 
 MODEL 
  
 `bqml_tutorial.ga_arima_model` 
 );

    The ar_coefficients output column shows the model coefficients of the autoregressive (AR) part of the ARIMA model. Similarly, the ma_coefficients output column shows the model coefficients of the moving-average (MA) part of the ARIMA model. Both of these columns contain array values, whose lengths are equal to non_seasonal_p and non_seasonal_q , respectively. You saw in the output of the ML.ARIMA_EVALUATE function that the best model has a non_seasonal_p value of 2 and a non_seasonal_q value of 3 . Therefore, in the ML.ARIMA_COEFFICIENTS output, the ar_coefficients value is a 2-element array and the ma_coefficients value is a 3-element array. The intercept_or_drift value is the constant term in the ARIMA model.

    For more information about the output columns, see ML.ARIMA_COEFFICIENTS function .

    BigQuery DataFrames

    Inspect the time series model's coefficients by using the coef_ function.

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      coef 
 = 
 model 
 . 
 coef_ 
 print 
 ( 
 coef 
 . 
 peek 
 ()) 
 # Expected output: 
 #       ar_coefficients   ma_coefficients   intercept_or_drift 
 #   0	 [0.40944762]	   [-0.81168198]	      0.0

    The ar_coefficients output column shows the model coefficients of the autoregressive (AR) part of the ARIMA model. Similarly, the ma_coefficients output column shows the model coefficients of the moving-average (MA) part of the ARIMA model. Both of these columns contain array values, whose lengths are equal to non_seasonal_p and non_seasonal_q , respectively.

    Use the model to forecast data

    SQL

    Forecast future time series values by using the ML.FORECAST function.

    In the following GoogleSQL query, the STRUCT(30 AS horizon, 0.8 AS confidence_level) clause indicates that the query forecasts 30 future time points, and generates a prediction interval with an 80% confidence level.

    Follow these steps to forecast data with the model:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, paste in the following query and click Run:

       SELECT 
 * 
 FROM 
 ML 
 . 
 FORECAST 
 ( 
 MODEL 
  
 `bqml_tutorial.ga_arima_model` 
 , 
  
 STRUCT 
 ( 
 30 
  
 AS 
  
 horizon 
 , 
  
 0.8 
  
 AS 
  
 confidence_level 
 ));

      The results should look similar to the following:

      ML.FORECAST output.

    BigQuery DataFrames

    Forecast future time series values by using the predict function.

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      prediction 
 = 
 model 
 . 
 predict 
 ( 
 horizon 
 = 
 30 
 , 
 confidence_level 
 = 
 0.8 
 ) 
 print 
 ( 
 prediction 
 . 
 peek 
 ()) 
 # Expected output: 
 #           forecast_timestamp	   forecast_value	standard_error	confidence_level	prediction_interval_lower_bound	    prediction_interval_upper_bound	    confidence_interval_lower_bound	    confidence_interval_upper_bound 
 # 11	2017-08-13 00:00:00+00:00	1845.439732	      328.060405	      0.8	                 1424.772257	                      2266.107208	                     1424.772257	                     2266.107208 
 # 29	2017-08-31 00:00:00+00:00	2615.993932	      431.286628	      0.8	                 2062.960849	                      3169.027015	                     2062.960849	                     3169.027015 
 # 7	    2017-08-09 00:00:00+00:00	2639.285993	      300.301186	      0.8	                 2254.213792	                      3024.358193	                     2254.213792	                     3024.358193 
 # 25	2017-08-27 00:00:00+00:00	1853.735689	      410.596551	      0.8	                 1327.233216	                      2380.238162	                     1327.233216	                     2380.238162 
 # 1	    2017-08-03 00:00:00+00:00	2621.33159	      241.093355	      0.8	                 2312.180802	                      2930.482379	                     2312.180802	                     2930.482379

    The output rows are in chronological order by the forecast_timestamp column value. In time series forecasting, the prediction interval, as represented by the prediction_interval_lower_bound and prediction_interval_upper_bound column values, is as important as the forecast_value column value. The forecast_value value is the middle point of the prediction interval. The prediction interval depends on the standard_error and confidence_level column values.

    For more information about the output columns, see ML.FORECAST function .

    Explain the forecasting results

    SQL

    You can get explainability metrics in addition to forecast data by using the ML.EXPLAIN_FORECAST function. The ML.EXPLAIN_FORECAST function forecasts future time series values and also returns all the separate components of the time series.

    Similar to the ML.FORECAST function, the STRUCT(30 AS horizon, 0.8 AS confidence_level) clause used in the ML.EXPLAIN_FORECAST function indicates that the query forecasts 30 future time points and generates a prediction interval with 80% confidence.

    Follow these steps to explain the model's results:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, paste in the following query and click Run:

       SELECT 
 * 
 FROM 
 ML 
 . 
 EXPLAIN_FORECAST 
 ( 
 MODEL 
  
 `bqml_tutorial.ga_arima_model` 
 , 
  
 STRUCT 
 ( 
 30 
  
 AS 
  
 horizon 
 , 
  
 0.8 
  
 AS 
  
 confidence_level 
 ));

      The results should look similar to the following:

      The first nine output columns of forecasted data and forecast explanations.The tenth through seventeenth output columns of forecasted data and forecast explanations.The last six output columns of forecasted data and forecast explanations.

      The output rows are ordered chronologically by the time_series_timestamp column value.

      For more information about the output columns, see ML.EXPLAIN_FORECAST function .

    BigQuery DataFrames

    You can get explainability metrics in addition to forecast data by using the predict_explain function. The predict_explain function forecasts future time series values and also returns all the separate components of the time series.

    Similar to the predict function, the horizon=30, confidence_level=0.8 clause used in the predict_explain function indicates that the query forecasts 30 future time points and generates a prediction interval with 80% confidence.

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      ex_pred 
 = 
 model 
 . 
 predict_explain 
 ( 
 horizon 
 = 
 30 
 , 
 confidence_level 
 = 
 0.8 
 ) 
 print 
 ( 
 ex_pred 
 . 
 head 
 ( 
 4 
 )) 
 # Expected output: 
 #       time_series_timestamp	  time_series_type	    time_series_data	time_series_adjusted_data	 standard_error	   confidence_level	   prediction_interval_lower_bound	   prediction_interval_upper_bound	  trend	   seasonal_period_yearly	  seasonal_period_quarterly	    seasonal_period_monthly	   seasonal_period_weekly	  seasonal_period_daily	    holiday_effect	   spikes_and_dips	   step_changes	   residual 
 # 0	  2016-08-01 00:00:00+00:00	      history	             1711.0	               505.716474	           206.939556	         <NA>	                    <NA>	                            <NA>	               0.0	           <NA>	                        <NA>	                     <NA>	                 169.611938	                  <NA>	                <NA>	            <NA>	       1205.283526	   336.104536 
 # 1	  2016-08-02 00:00:00+00:00	      history	             2140.0	               623.137701	           206.939556	         <NA>	                    <NA>	                            <NA>	            336.104428	       <NA>	                        <NA>	                     <NA>	                 287.033273	                  <NA>	                <NA>	            <NA>	       1205.283526	   311.578773 
 # 2	  2016-08-03 00:00:00+00:00	      history	             2890.0	               1008.655091	           206.939556	         <NA>	                    <NA>	                            <NA>	            563.514213	       <NA>	                        <NA>	                     <NA>	                 445.140878	                  <NA>	                <NA>	            <NA>	       1205.283526	   676.061383 
 # 3	  2016-08-04 00:00:00+00:00	      history	             3161.0	               1389.40959	           206.939556	         <NA>	                    <NA>	                            <NA>	            986.317236	       <NA>	                        <NA>	                     <NA>	                 403.092354	                  <NA>	                <NA>	            <NA>	       1205.283526	   566.306884 
 # 4	  2016-08-05 00:00:00+00:00	      history	             2702.0	               1394.395741	           206.939556	         <NA>	                    <NA>	                            <NA>	            1248.707386	       <NA>	                        <NA>	                     <NA>	                 145.688355	                  <NA>	                <NA>	            <NA>	       1205.283526	   102.320733 
 # 5	  2016-08-06 00:00:00+00:00	      history	             1663.0	               437.09243	           206.939556	         <NA>	                    <NA>	                            <NA>	            1188.59004	       <NA>	                        <NA>	                     <NA>	                 -751.49761	                  <NA>	                <NA>	            <NA>	       1205.283526	    20.624044

    If you would like to visualize the results, you can use Data Studio as described in the Visualize the input data section to create a chart, using the following columns as metrics:

    • time_series_data
    • prediction_interval_lower_bound
    • prediction_interval_upper_bound
    • trend
    • seasonal_period_weekly
    • step_changes

    Clean up

    To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

    • You can delete the project you created.
    • Or you can keep the project and delete the dataset.

    Delete your dataset

    Deleting your project removes all datasets and all tables in the project. If you prefer to reuse the project, you can delete the dataset you created in this tutorial:

    1. If necessary, open the BigQuery page in the Google Cloud console.

      Go to the BigQuery page

    2. In the navigation, click the bqml_tutorialdataset you created.

    3. Click Delete dataseton the right side of the window. This action deletes the dataset, the table, and all the data.

    4. In the Delete datasetdialog box, confirm the delete command by typing the name of your dataset ( bqml_tutorial ) and then click Delete.

    Delete your project

    To delete the project:

    1. In the Google Cloud console, go to the Manage resources page.

      Go to Manage resources

    2. In the project list, select the project that you want to delete, and then click Delete .
    3. In the dialog, type the project ID, and then click Shut down to delete the project.

    What's next

    Design a Mobile Site
    View Site in Mobile | Classic
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