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vastorbit.machine_learning.vast.tsa.VAR.deploySQL

VAR.deploySQL(ts: str | None = None, y: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, start: int | None = None, npredictions: int = 10, output_standard_errors: bool = False, output_index: bool = False, use_index_as_suffix: bool = False) → str

Returns the SQL code needed to deploy the model.

For AR/VAR models trained with pure SQL, this generates SQL to compute predictions using the learned coefficients.

Parameters:

  • ts (str, optional) – TS (Time Series) VastColumn used to order the data.

  • y (SQLColumns, optional) – Response column(s).

  • start (int, optional) – Starting position for prediction.

  • npredictions (int, optional) – Number of predicted timesteps.

  • output_standard_errors (bool, optional) – Whether to return standard error estimates (not supported).

  • output_index (bool, optional) – Whether to return the index of each position.

  • use_index_as_suffix (bool, optional) – For multivariate models, use indexes as suffix instead of names.

Returns:

SQL code for prediction.

Return type:

str

Raises:

NotImplementedError – For ARIMA models, as they require iterative calculations.