datasetsforecast.m5
M5
__init__
download
directory
(str): Directory path to download dataset.load
directory
(str): Directory where data will be downloaded.cache
(bool): If True
saves and loads.M5Evaluation
aggregate_levels
y_hat
(pd.DataFrame): Forecasts as wide pandas dataframe with columns [‘unique_id’].categories
(pd.DataFrame, optional): Categories of M5 dataset (not used). Defaults to None.pd.DataFrame
: Aggregated forecasts as wide pandas dataframe with columns [‘unique_id’].evaluate
directory
(str): Directory where data will be downloaded.validation
(bool): Wheter perform validation evaluation. Default False, return test evaluation.y_hat
(Union[pd.DataFrame, str]): Forecasts as wide pandas dataframe with columns [‘unique_id’] and forecasts or benchmark url fromhttps
: //github.com/Nixtla/m5-forecasts/tree/main/forecasts.pd.DataFrame
: DataFrame with columns OWA, SMAPE, MASE and group as index.load_benchmark
directory
(str): Directory where data will be downloaded.source_url
(str, optional): Optional benchmark url obtained fromhttps
: //github.com/Nixtla/m5-forecasts/tree/master/forecasts. If None
returns the M5 winner.validation
(bool): Wheter return validation forecasts. Default False, return test forecasts.np.ndarray
: Numpy array of shape (n_series, horizon).