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 
 ` 
 ) 

What's next

• For information about model weights support in BigQuery ML, see BigQuery ML model weights overview .
• For more information about supported SQL statements and functions for time series forecasting models, see End-to-end user journeys for time series forecasting models .