<model_name>!FORECAST¶

Syntax¶

The required arguments vary depending on what use case the model was trained for.

For single-series models without exogenous variables:

<name>!FORECAST(
  FORECASTING_PERIODS => <forecasting_periods>,
  [ CONFIG_OBJECT => <config_object> ]
);

For single-series models with exogenous variables:

<name>!FORECAST(
  INPUT_DATA => <input_data>,
  TIMESTAMP_COLNAME => '<timestamp_colname>',
  [ CONFIG_OBJECT => <config_object> ]
);

For multiple-series models without exogenous variables:

<name>!FORECAST(
  SERIES_VALUE => <series>,
  FORECASTING_PERIODS => <forecasting_periods>,
  [ CONFIG_OBJECT => <config_object> ]
);

For multiple-series models with exogenous variables:

<name>!FORECAST(
  SERIES_VALUE => <series>,
  SERIES_COLNAME => <series_colname>,
  INPUT_DATA => <input_data>,
  TIMESTAMP_COLNAME => '<timestamp_colname>',
  [ CONFIG_OBJECT => <config_object> ]
);

Arguments¶

Required:

Not all of the following arguments are required for every use case.

FORECASTING_PERIODS => forecasting_periods

Required for forecasts without exogenous variables.

The number of steps ahead to forecast. The interval between steps is inferred by the model during training.

INPUT_DATA => input_data

Required for forecasts with exogenous variables.

A reference to a table, view, or query that contains the future timestamps and values of the exogenous variables (additional user-provided features) that were passed as input_data when training the model. Using a reference allows the forecasting process, which runs with limited privileges, to use your privileges to access the data. Columns are matched between this argument and the original exogenous training data by name.

TIMESTAMP_COLNAME => 'timestamp_colname'

Required for forecasts with exogenous variables.

The name of the column in input_data containing the timestamps.

SERIES_COLNAME => 'series_colname'

Required for multi-series forecasts with exogenous variables.

The name of the column in input_data specifying the series.

SERIES_VALUE => series

Required for multi-series forecasts.

The time series to forecast. Can be a single value (e.g., 'Series A'::variant) or a VARIANT, but must specify a series that the model has been trained on. If not specified, all trained series are predicted.

Optional:

CONFIG_OBJECT => config_object

An OBJECT containing key-value pairs used to configure the forecast job.

Key	Type	Default	Description
prediction_interval	FLOAT	0.95	A value greater than or equal to 0.0 and less than 1.0. The default value of 0.95 means 95% of future points are expected to fall within the interval [lower_bound, upper_bound] from the forecast result.
on_error	STRING	'ABORT'	String (constant) specifying the error handling method. This is most useful when forecasting multiple series. Supported values are: 'abort': Abort the model forecasting operation if an error is encountered in any time series. 'skip': Skip any time series where forecasting encounters an error. This allows forecasting to succeed for other time series. Series that failed are absent from the model output.

Output¶

Examples¶

See Examples.