Limit forecasted values for an ARIMA_PLUS time series model

This tutorial teaches you how to use limits to narrow the forecasted results returned by an ARIMA_PLUS time series model. In this tutorial, you create two time series models over the same data, one model which uses limits and one model that doesn't use limits. This lets you compare the results returned by the models and understand the difference that specifying limits makes.

You use the new_york.citibike_trips data to train the models in this tutorial. This dataset contains information about Citi Bike trips in New York City.

Before following this tutorial, you should be familiar with single time series forecasting. Complete the Single time series forecasting from Google Analytics data tutorial for an introduction to this topic.

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.

Objectives

In this tutorial, you use the following:

• The CREATE MODEL statement: to create a time series model.
• The ML.FORECAST function: to forecast daily total visits.

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

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.

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.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the BigQuery API.

Roles required to enable APIs

To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

Enable the API

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.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the BigQuery API.

Roles required to enable APIs

To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

Enable the API

Create a dataset

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

import google.cloud.bigquery

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

Visualize the time series you want to forecast

Before creating the model, it is useful to see what your input time series looks like.

#standardSQL
SELECT
  EXTRACT(DATE from starttime) AS date,
  COUNT(*) AS num_trips
FROM
`bigquery-public-data`.new_york.citibike_trips
GROUP BY date

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"]).count()

num_trips.plot.line()

Create a time series model

Create a time series model, using the NYC Citi Bike trips data.

The following GoogleSQL query creates a model that forecasts daily total bike trips. The CREATE MODEL statement creates and trains a model named bqml_tutorial.nyc_citibike_arima_model.

#standardSQL
CREATE OR REPLACE MODEL bqml_tutorial.nyc_citibike_arima_model
  OPTIONS (
    model_type = 'ARIMA_PLUS',
    time_series_timestamp_col = 'date',
    time_series_data_col = 'num_trips',
    time_series_id_col = 'start_station_id')
AS
SELECT
  EXTRACT(DATE FROM starttime) AS date,
  COUNT(*) AS num_trips,
  start_station_id
FROM
  `bigquery-public-data`.new_york.citibike_trips
WHERE starttime > '2014-07-11' AND starttime  '2014-07-11' AND starttime  '2014-07-11'
           AND starttime  '2014-07-11' AND starttime  '2014-07-11' AND starttime  '2014-07-11'
           AND starttime 


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