fetch_tourism_monthly¶

`yohou.datasets._fetchers.fetch_tourism_monthly(*, n_series=None, data_home=None, download_if_missing=True, n_retries=3, delay=1.0)` ¶

Fetch the Tourism Monthly dataset from Monash/Zenodo.

366 monthly tourism time series from the tourism forecasting competition.

Parameters¶

Name	Type	Description	Default
`n_series`	`int or None`	Maximum number of series to include. `None` loads all 366 series. A smaller value reduces memory usage and speeds up parsing.	`None`
`data_home`	`str, PathLike, or None`	Specify another download and cache folder for the datasets. By default all yohou data is stored in `~/yohou_data/`.	`None`
`download_if_missing`	`bool`	If `False`, raise an `OSError` if the data is not locally available instead of trying to download it.	`True`
`n_retries`	`int`	Number of retries when HTTP errors are encountered.	`3`
`delay`	`float`	Number of seconds between retries.	`1.0`

Returns¶

Type Description

Bunch

Dictionary-like object with the following attributes:

frame : pl.DataFrame DataFrame with "time" (Datetime) and up to 366 series columns using the __ separator convention (e.g. "T1__tourists"). feature_names : list of str Non-time column names. DESCR : str Full description of the dataset. frequency : str "1mo". n_series : int Number of series actually loaded. filename : str Path to the cached parquet file.

References¶

[1] Godahewa, R., Bergmeir, C., Webb, G. I., Hyndman, R. J., & Montero-Manso, P. (2021). "Monash Time Series Forecasting Archive." Neural Information Processing Systems Track on Datasets and Benchmarks. https://doi.org/10.5281/zenodo.4656096

Examples¶

>>> from yohou.datasets import fetch_tourism_monthly
>>> bunch = fetch_tourism_monthly()
>>> bunch.frame.columns[:2]
['time', 'T1__tourists']

Source Code¶

View on GitHub

Show/Hide sourcedef fetch_tourism_monthly(
    *,
    n_series: int | None = None,
    data_home: str | os.PathLike | None = None,
    download_if_missing: bool = True,
    n_retries: int = 3,
    delay: float = 1.0,
) -> Bunch:
    """Fetch the Tourism Monthly dataset from Monash/Zenodo.

    366 monthly tourism time series from the tourism forecasting
    competition.

    Parameters
    ----------
    n_series : int or None, default=None
        Maximum number of series to include.  ``None`` loads all 366
        series.  A smaller value reduces memory usage and speeds up
        parsing.
    data_home : str, PathLike, or None
        Specify another download and cache folder for the datasets.
        By default all yohou data is stored in ``~/yohou_data/``.
    download_if_missing : bool, default=True
        If ``False``, raise an ``OSError`` if the data is not locally
        available instead of trying to download it.
    n_retries : int, default=3
        Number of retries when HTTP errors are encountered.
    delay : float, default=1.0
        Number of seconds between retries.

    Returns
    -------
    Bunch
        Dictionary-like object with the following attributes:

        frame : pl.DataFrame
            DataFrame with ``"time"`` (Datetime) and up to 366 series
            columns using the ``__`` separator convention
            (e.g. ``"T1__tourists"``).
        feature_names : list of str
            Non-time column names.
        DESCR : str
            Full description of the dataset.
        frequency : str
            ``"1mo"``.
        n_series : int
            Number of series actually loaded.
        filename : str
            Path to the cached parquet file.

    See Also
    --------
    - [`fetch_tourism_quarterly`][yohou.datasets._fetchers.fetch_tourism_quarterly] : Quarterly tourism series from the same competition.
    - [`fetch_hospital`][yohou.datasets._fetchers.fetch_hospital] : Monthly hospital patient count series.
    - [`get_data_home`][yohou.datasets._fetchers.get_data_home] : Return the path of the data directory.

    References
    ----------
    [1] Godahewa, R., Bergmeir, C., Webb, G. I., Hyndman, R. J., &
        Montero-Manso, P. (2021). "Monash Time Series Forecasting Archive."
        Neural Information Processing Systems Track on Datasets and
        Benchmarks. https://doi.org/10.5281/zenodo.4656096

    Examples
    --------
    >>> from yohou.datasets import fetch_tourism_monthly
    >>> bunch = fetch_tourism_monthly()  # doctest: +SKIP
    >>> bunch.frame.columns[:2]  # doctest: +SKIP
    ['time', 'T1__tourists']

    """
    return _fetch_dataset(
        metadata=TOURISM_MONTHLY,
        dataset_name="tourism_monthly",
        value_column_name="tourists",
        n_series=n_series,
        data_home=data_home,
        download_if_missing=download_if_missing,
        n_retries=n_retries,
        delay=delay,
    )

Tutorials¶

The following example notebooks use this component:

How to Forecast with CatBoost

Forecasting-Models

Plug CatBoostRegressor into PointReductionForecaster as a drop-in sklearn estimator, compare gradient-boosted versus Ridge linear baseline, and demonstrate the direct reduction strategy with tree-based models.

View · Open in marimo
Panel Data Forecasting

Getting-Started

Forecast multiple related time series simultaneously using the __ naming convention, LocalPanelForecaster, and per-group scoring.

View · Open in marimo