The ML.ARIMA_COEFFICIENTS function

This document describes the ML.ARIMA_COEFFICIENTS function, which lets you see the ARIMA coefficients and the weights of the external regressors for ARIMA_PLUS and ARIMA_PLUS_XREG time series models.

Syntax

ML.ARIMA_COEFFICIENTS(
  MODEL `PROJECT_ID.DATASET.MODEL`
)

Arguments

ML.ARIMA_COEFFICIENTS takes the following arguments:

  • PROJECT_ID: your project ID.
  • DATASET: the BigQuery dataset that contains the model.
  • MODEL: the name of the model.

Output

ML.ARIMA_COEFFICIENTS returns the following columns:

  • time_series_id_col or time_series_id_cols: a value that contains the identifiers of a time series. time_series_id_col can be an INT64 or STRING value. time_series_id_cols can be an ARRAY<INT64> or ARRAY<STRING> value. Only present when forecasting multiple time series simultaneously. The column names and types are inherited from the TIME_SERIES_ID_COL option as specified in the model creation query.
  • ar_coefficients: an ARRAY<FLOAT64> value that contains the autoregressive coefficients, which corresponds to non-seasonal p.
  • ma_coefficients: an ARRAY<FLOAT64> value that contains the moving-average coefficients, which corresponds to non-seasonal q.
  • intercept_or_drift: a FLOAT64 value that contains the constant term of the ARIMA model. By definition, the constant term is called intercept when non-seasonal d is 0, and drift when non-seasonal d is 1. intercept_or_drift is always 0 when non-seasonal d is 2.
  • processed_input: a STRING value that contains the name of the model feature input column. The value of this column matches the name of the feature column provided in the query_statement clause that was used when the model was trained.
  • weight: when the processed_input value is numerical, weight contains a FLOAT64 value and the category_weights column contains NULL values. When the processed_input value is non-numerical and has been converted to dummy encoding, the weight column is NULL and the category_weights column contains the category names and weights for each category.
  • category_weights.category: a STRING value that contains the category name if the processed_input value is non-numeric.
  • category_weights.weight: a FLOAT64 that contains the category's weight if the processed_input value is non-numeric.

Example

The following example retrieves the model coefficients information from the model mydataset.mymodel in your default project:

SELECT
  *
FROM
  ML.ARIMA_COEFFICIENTS(MODEL `mydataset.mymodel`)

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