Use custom holidays in an ARIMA_PLUS time-series forecasting model

This tutorial shows you how to do the following tasks:

  • Create an ARIMA_PLUS time-series forecasting model that uses only built-in holidays.
  • Create an ARIMA_PLUS time-series forecasting model that uses custom holidays in addition to built-in holidays.
  • Visualize the forecasted results from these models.
  • Inspect a model to see which holidays it models.
  • Evaluate the effects of the custom holidays on the forecasted results.
  • Compare the performance of the model that uses only built-in holidays to the performance of the model that uses custom holidays in addition to built-in holidays.

This tutorial uses the bigquery-public-data.wikipedia.pageviews_* public tables.

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.

Costs

In this document, you use the following billable components of Google Cloud:

  • BigQuery: You incur costs for the data you process in BigQuery.

To generate a cost estimate based on your projected usage, use the pricing calculator.

New Google Cloud users might be eligible for a free trial.

For more information, see BigQuery 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ドル 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 (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. 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

  5. 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 (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

    Go to project selector

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

  7. 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

Create a dataset

Create a BigQuery dataset to store your ML model.

Console

  1. In the Google Cloud console, go to the BigQuery page.

    Go to the BigQuery page

  2. In the Explorer pane, click your project name.

  3. Click View actions > Create dataset

  4. On the Create dataset page, do the following:

    • For Dataset ID, enter bqml_tutorial.

    • For Location type, select Multi-region, and then select US (multiple regions in United States).

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

bq

To create a new dataset, use the bq mk command with the --location flag. For a full list of possible parameters, see the bq mk --dataset command reference.

  1. Create a dataset named bqml_tutorial with the data location set to US and a description of BigQuery ML tutorial dataset:

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

    Instead of using the --dataset flag, the command uses the -d shortcut. If you omit -d and --dataset, the command defaults to creating a dataset.

  2. Confirm that the dataset was created:

    bqls

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. 

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

Prepare the time-series data

Aggregate the Wikipedia page view data for the Google I/O page into a single table, grouped by day:

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the SQL editor pane, run the following SQL statement:

    CREATEORREPLACETABLE`bqml_tutorial.googleio_page_views`
AS
SELECT
DATETIME_TRUNC(datehour,DAY)ASdate,
SUM(views)ASviews
FROM
`bigquery-public-data.wikipedia.pageviews_*`
WHERE
datehour>='2017-01-01'
ANDdatehour<'2023-01-01'
ANDtitle='Google_I/O'
GROUPBY
DATETIME_TRUNC(datehour,DAY)

Create a time-series forecasting model that uses built-in holidays

Create a model that forecasts daily page views for the Wikipedia "Google I/O" page, based on page view data before 2022 and taking built-in holidays into account:

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the SQL editor pane, run the following SQL statement:

    CREATEORREPLACEMODEL`bqml_tutorial.forecast_googleio`
OPTIONS(
model_type='ARIMA_PLUS',
holiday_region='US',
time_series_timestamp_col='date',
time_series_data_col='views',
data_frequency='DAILY',
horizon=365)
AS
SELECT
*
FROM
`bqml_tutorial.googleio_page_views`
WHERE
date<'2022-01-01';

Visualize the forecasted results

After you create the model using built-in holidays, join the original data from the bqml_tutorial.googleio_page_views table with the forecasted value from the ML.EXPLAIN_FORECAST function, and then visualize it by using Looker Studio:

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the SQL editor pane, run the following SQL statement:

    SELECT
original.date,
original.viewsASoriginal_views,
explain_forecast.time_series_adjusted_data
ASadjusted_views_without_custom_holiday,
FROM
`bqml_tutorial.googleio_page_views`original
INNERJOIN
(
SELECT
*
FROM
ML.EXPLAIN_FORECAST(
MODEL`bqml_tutorial.forecast_googleio`,
STRUCT(365AShorizon))
)explain_forecast
ON
TIMESTAMP(original.date)
=explain_forecast.time_series_timestamp
ORDERBY
original.date;

  3. In the Query results pane, click Open in > Looker Studio. Looker Studio opens in a new tab.

  4. In the Looker Studio tab, click Add a chart, and then click the time series chart:

    Add a time series chart.

    Place the chart on the report.

  5. On the Setup tab of the Chart pane, click Add metric and select adjusted_views_without_custom_holiday:

    Add an additional metric.

    The chart looks similar to the following:

    Time-series chart of forecasting results using built-in holidays

    You can see that the forecasting model captures the general trend pretty well. However, it isn't capturing the increased traffic related to previous Google I/O events, and it isn't able to generate an accurate forecast for

    1. The next sections show you how to deal with some of these limitations.

Create a time-series forecasting model that uses built-in holidays and custom holidays

As you can see in Google I/O history, the Google I/O event occurred on different dates between 2017 and 2022. To take this variation into account, create a model that forecasts page views for the Wikipedia "Google_I/O" page through 2022, based on page view data from before 2022, and using custom holidays to represent the Google I/O event each year. In this model, you also adjust the holiday effect window to cover three days around the event date, to better capture some potential page traffic before and after the event.

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the SQL editor pane, run the following SQL statement:

    CREATEORREPLACEMODEL`bqml_tutorial.forecast_googleio_with_custom_holiday`
OPTIONS(
model_type='ARIMA_PLUS',
holiday_region='US',
time_series_timestamp_col='date',
time_series_data_col='views',
data_frequency='DAILY',
horizon=365)
AS(
training_dataAS(
SELECT
*
FROM
`bqml_tutorial.googleio_page_views`
WHERE
date<'2022-01-01'
),
custom_holidayAS(
SELECT
'US'ASregion,
'GoogleIO'ASholiday_name,
primary_date,
1ASpreholiday_days,
2ASpostholiday_days
FROM
UNNEST(
[
DATE('2017-05-17'),
DATE('2018-05-08'),
DATE('2019-05-07'),
-- cancelled in 2020 due to pandemic
DATE('2021-05-18'),
DATE('2022-05-11')])
ASprimary_date
)
);

Visualize the forecasted results

After you create the model using custom holidays, join the original data from the bqml_tutorial.googleio_page_views table with the forecasted value from the ML.EXPLAIN_FORECAST function, and then visualize it by using Looker Studio:

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the SQL editor pane, run the following SQL statement:

    SELECT
original.date,
original.viewsASoriginal_views,
explain_forecast.time_series_adjusted_data
ASadjusted_views_with_custom_holiday,
FROM
`bqml_tutorial.googleio_page_views`original
INNERJOIN
(
SELECT
*
FROM
ML.EXPLAIN_FORECAST(
MODEL
`bqml_tutorial.forecast_googleio_with_custom_holiday`,
STRUCT(365AShorizon))
)explain_forecast
ON
TIMESTAMP(original.date)
=explain_forecast.time_series_timestamp
ORDERBY
original.date;

  3. In the Query results pane, click Explore data, and then click Explore with Looker Studio. Looker Studio opens in a new tab.

  4. In the Looker Studio tab, click Add a chart, click the time series chart, and place the chart on the report.

  5. On the Setup tab of the Chart pane, click Add metric and select adjusted_views_with_custom_holiday.

    The chart looks similar to the following:

    Time-series chart of forecasting results using custom holidays

    As you can see, the custom holidays boosted the performance of the forecasting model. It now effectively captures the increase of page views caused by Google I/O.

Inspect holiday information

Inspect the list of holidays that were taken into account during modeling by using the ML.HOLIDAY_INFO function:

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the SQL editor pane, run the following SQL statement:

    SELECT*
FROM
ML.HOLIDAY_INFO(
MODEL`bqml_tutorial.forecast_googleio_with_custom_holiday`);

    The results show both Google I/O and the built-in holidays in the list of holidays:

    Results from the ML.HOLIDAY_INFO function.

Evaluate the effects of the custom holidays

Evaluate the effects of the custom holidays on the forecasted results by using the ML.EXPLAIN_FORECAST function:

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the SQL editor pane, run the following SQL statement:

    SELECT
time_series_timestamp,
holiday_effect_GoogleIO,
holiday_effect_US_Juneteenth,
holiday_effect_Christmas,
holiday_effect_NewYear
FROM
ML.EXPLAIN_FORECAST(
model
`bqml_tutorial.forecast_googleio_with_custom_holiday`,
STRUCT(365AShorizon))
WHEREholiday_effect!=0;

    The results show that Google I/O contributes a large amount of holiday effect to the forecasted results:

    Results from the ML.EXPLAIN_FORECAST function.

Compare model performance

Use the ML.EVALUATE function to compare the performance of the first model created without custom holidays and the second model created with custom holidays. To see how the second model performs when it comes to forecasting a future custom holiday, set the time range to the week of Google I/O in 2022:

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the SQL editor pane, run the following SQL statement:

    SELECT
"original"ASmodel_type,
*
FROM
ml.evaluate(
MODEL`bqml_tutorial.forecast_googleio`,
(
SELECT
*
FROM
`bqml_tutorial.googleio_page_views`
WHERE
date>='2022-05-08'
ANDdate<'2022-05-12'
),
STRUCT(
365AShorizon,
TRUEASperform_aggregation))
UNIONALL
SELECT
"with_custom_holiday"ASmodel_type,
*
FROM
ml.evaluate(
MODEL
`bqml_tutorial.forecast_googleio_with_custom_holiday`,
(
SELECT
*
FROM
`bqml_tutorial.googleio_page_views`
WHERE
date>='2022-05-08'
ANDdate<'2022-05-12'
),
STRUCT(
365AShorizon,
TRUEASperform_aggregation));

    The results show that the second model offers a significant performance improvement:

    Results from the ML.EXPLAIN_FORECAST function.

Clean up

  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.

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Last updated 2025年10月30日 UTC.