predict

TimeSeriesEndpoint.predict(data: str | DataFrame, known_covariates: str | DataFrame | None = None, static_features: str | DataFrame | None = None, prediction_length: int = 1, target: str = 'target', id_column: str = 'item_id', timestamp_column: str = 'timestamp', quantile_levels: List[float] | None = None, accept: str = 'application/x-parquet') → DataFrame

Run real-time prediction on the deployed endpoint.

Parameters:

• data – Historical time series to forecast from, in long format, as a DataFrame or local/S3 path to a data file. See the TimeSeriesPredictor docs for the expected format.

• known_covariates – Future values of the known covariates over the forecast horizon.

• static_features – Static (time-independent) features describing each individual time series.

• prediction_length – Forecast horizon: how many time steps into the future the model should predict.

• target – Name of the column that contains the target values to forecast.

• id_column – Name of the column with the unique identifier of each time series (item).

• timestamp_column – Name of the column with the observation timestamps.

• quantile_levels – List of increasing decimals between 0 and 1 specifying which quantiles to estimate. Defaults to [0.1, 0.2, ..., 0.9].

• accept – Response format. Options: ‘application/x-parquet’, ‘text/csv’, ‘application/json’.

Return type:

pd.DataFrame