The ML.ARIMA_EVALUATE function

This document describes the ML.ARIMA_EVALUATE function, which you can use to evaluate the model metrics of ARIMA_PLUS or ARIMA_PLUS_XREG time series models.

Syntax

ML.ARIMA_EVALUATE(
  MODEL `project_id.dataset.model`,
  [, STRUCT(show_all_candidates AS show_all_candidate_models)])

Arguments

ML.ARIMA_EVALUATE takes the following arguments:

Output

ML.ARIMA_EVALUATE returns the following columns:

The has_holiday_effect, has_spikes_and_dips, and has_step_changes columns are only populated for ARIMA_PLUS models that have decompose_time_series enabled.

All of the columns are specific to the fitted ARIMA models except for the following columns:

When the non_seasonal_d value is not 1, has_drift is set to FALSE by default, because has_drift doesn't apply in those cases.

Example

The following example retrieves the evaluation metrics of the best model from the model mydataset.mymodel in your default project:

SELECT
  *
FROM
  ML.ARIMA_EVALUATE(MODEL `mydataset.mymodel`, STRUCT(FALSE AS show_all_candidate_models))

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