TimeSeriesPredictor.evaluate#

TimeSeriesPredictor.evaluate(data: Union[TimeSeriesDataFrame, DataFrame], **kwargs)[source]#

Evaluate the performance for given dataset, computing the score determined by self.eval_metric on the given data set, and with the same prediction_length used when training models.

Parameters

  • data (Union[TimeSeriesDataFrame, pd.DataFrame]) –

    The data to evaluate the best model on. The last prediction_length time steps of the data set, for each item, will be held out for prediction and forecast accuracy will be calculated on these time steps.

    If known_covariates_names were specified when creating the predictor, data must include the columns listed in known_covariates_names with the covariates values aligned with the target time series.

    If train_data used to train the predictor contained past covariates or static features, then data must also include them (with same column names and dtypes).

    If provided data is an instance of pandas DataFrame, AutoGluon will attempt to automatically convert it to a TimeSeriesDataFrame.

  • model (str, optional) – Name of the model that you would like to evaluate. By default, the best model during training (with highest validation score) will be used.

  • metric (str, optional) – Name of the evaluation metric to compute scores with. Defaults to self.eval_metric

  • use_cache (bool, default = True) – If True, will attempt to use the cached predictions. If False, cached predictions will be ignored. This argument is ignored if cache_predictions was set to False when creating the TimeSeriesPredictor.

Returns

score – A forecast accuracy score, where higher values indicate better quality. For consistency, error metrics will have their signs flipped to obey this convention. For example, negative MAPE values will be reported.

Return type

float