bigframes.bigquery.ai.forecast#

bigframes.bigquery.ai.forecast(df: DataFrame | DataFrame, *, data_col: str, timestamp_col: str, model: str = 'TimesFM 2.0', id_cols: Iterable[str] | None = None, horizon: int = 10, confidence_level: float = 0.95, context_window: int | None = None) → DataFrame[source]#

Forecast time series at future horizon. Using Google Research’s open source TimesFM(google-research/timesfm) model.

Note

This product or feature is subject to the “Pre-GA Offerings Terms” in the General Service Terms section of the Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available “as is” and might have limited support. For more information, see the launch stage descriptions (https://cloud.google.com/products#product-launch-stages).

Parameters:

  • df (DataFrame) – The dataframe that contains the data that you want to forecast. It could be either a BigFrames Dataframe or a pandas DataFrame. If it’s a pandas DataFrame, the global BigQuery session will be used to load the data.

  • data_col (str) – A str value that specifies the name of the data column. The data column contains the data to forecast. The data column must use one of the following data types: INT64, NUMERIC and FLOAT64

  • timestamp_col (str) – A str value that specified the name of the time points column. The time points column provides the time points used to generate the forecast. The time points column must use one of the following data types: TIMESTAMP, DATE and DATETIME

  • model (str, default "TimesFM 2.0") – A str value that specifies the name of the model. TimesFM 2.0 is the only supported value, and is the default value.

  • id_cols (Iterable[str], optional) – An iterable of str value that specifies the names of one or more ID columns. Each ID identifies a unique time series to forecast. Specify one or more values for this argument in order to forecast multiple time series using a single query. The columns that you specify must use one of the following data types: STRING, INT64, ARRAY<STRING> and ARRAY<INT64>

  • horizon (int, default 10) – An int value that specifies the number of time points to forecast. The default value is 10. The valid input range is [1, 10,000].

  • confidence_level (float, default 0.95) – A FLOAT64 value that specifies the percentage of the future values that fall in the prediction interval. The default value is 0.95. The valid input range is [0, 1).

  • context_window (int, optional) – An int value that specifies the context window length used by BigQuery ML’s built-in TimesFM model. The context window length determines how many of the most recent data points from the input time series are use by the model. If you don’t specify a value, the AI.FORECAST function automatically chooses the smallest possible context window length to use that is still large enough to cover the number of time series data points in your input data.

Returns:

The forecast dataframe matches that of the BigQuery AI.FORECAST function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-forecast

Return type:

DataFrame

Raises:

ValueError – when any column ID does not exist in the dataframe.

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