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The ML.ARIMA_EVALUATE function | BigQuery

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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_NAME`,
  [, STRUCT(SHOW_ALL_CANDIDATE_MODELS AS show_all_candidate_models)])
Note: No input data is required. 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))
What's next

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-07 UTC.

[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-07 UTC."],[[["`ML.ARIMA_EVALUATE` is a function used to evaluate metrics for `ARIMA_PLUS` or `ARIMA_PLUS_XREG` time series models without requiring input data."],["The function's syntax includes specifying the model, project ID, and dataset, along with an optional boolean parameter `show_all_candidate_models` to display evaluation metrics for all models or just the best one."],["Output columns from `ML.ARIMA_EVALUATE` include identifiers, non-seasonal parameters (p, d, q), drift status, log-likelihood, AIC, variance, seasonal periods, and indicators for holiday effects, spikes, dips, and step changes, plus error messages."],["The `show_all_candidate_models` parameter determines whether metrics are returned for all candidate models or just the best model, and its default value differs between single time series models and large-scale time series models."],["The output metrics provided, are specific to the fitted `ARIMA` models, with the exception of the identifier columns (`time_series_id_col`, `time_series_id_cols`) and a few others that relay information on the time series history."]]],[]]


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