gluonts.dataset.repository package#

gluonts.dataset.repository.get_dataset(dataset_name: str, path: Path = PosixPath('/home/runner/.gluonts/datasets'), regenerate: bool = False, dataset_writer: DatasetWriter = JsonLinesWriter(use_gzip=True, suffix='.json', compresslevel=4), prediction_length: Optional[int] = None) → TrainDatasets[source]#

Get a repository dataset.

The datasets that can be obtained through this function have been used with different processing over time by several papers (e.g., [SFG17], [LCY+18], and [YRD15]) or are obtained through the Monash Time Series Forecasting Repository.

Parameters:

  • dataset_name – Name of the dataset, for instance “m4_hourly”.

  • regenerate – Whether to regenerate the dataset even if a local file is present. If this flag is False and the file is present, the dataset will not be downloaded again.

  • path – Where the dataset should be saved.

  • prediction_length – The prediction length to be used for the dataset. If None, the default prediction length will be used. If the dataset is already materialized, setting this option to a different value does not have an effect. Make sure to set regenerate=True in this case. Note that some datasets from the Monash Time Series Forecasting Repository do not actually have a default prediction length – the default then depends on the frequency of the data: - Minutely data –> prediction length of 60 (one hour) - Hourly data –> prediction length of 48 (two days) - Daily data –> prediction length of 30 (one month) - Weekly data –> prediction length of 8 (two months) - Monthly data –> prediction length of 12 (one year) - Yearly data –> prediction length of 4 (four years)

Return type:

Dataset obtained by either downloading or reloading from local file.

gluonts.dataset.repository.get_download_path() → Path[source]#
Returns:

default path to download datasets or models of gluon-ts. The path is $HOME/.gluonts/

Return type:

Path

Submodules#