fetch_sunspot

yohou.datasets._fetchers.fetch_sunspot(*, data_home=None, download_if_missing=True, n_retries=3, delay=1.0)

Fetch the Sunspot dataset (without missing values) from Monash/Zenodo.

Single daily time series of sunspot numbers from 1818-01-08 to 2020-05-31 (73 924 observations).

Parameters

Name	Type	Description	Default
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 "sunspot_number" (Float64). feature_names : list of str ["sunspot_number"]. DESCR : str Full description of the dataset. frequency : str "1d". n_series : int 1. filename : str Path to the cached parquet file.

See Also

• fetch_tourism_monthly : Monthly tourism series.
• fetch_electricity_demand : Half-hourly electricity demand series.
• 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.4654722

Examples

>>> from yohou.datasets import fetch_sunspot
>>> bunch = fetch_sunspot()
>>> bunch.frame.columns
['time', 'sunspot_number']

Source Code

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def fetch_sunspot(
    *,
    data_home: str | os.PathLike | None = None,
    download_if_missing: bool = True,
    n_retries: int = 3,
    delay: float = 1.0,
) -> Bunch:
    """Fetch the Sunspot dataset (without missing values) from Monash/Zenodo.

    Single daily time series of sunspot numbers from 1818-01-08 to
    2020-05-31 (73 924 observations).

    Parameters
    ----------
    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
            ``"sunspot_number"`` (Float64).
        feature_names : list of str
            ``["sunspot_number"]``.
        DESCR : str
            Full description of the dataset.
        frequency : str
            ``"1d"``.
        n_series : int
            ``1``.
        filename : str
            Path to the cached parquet file.

    See Also
    --------
    - [`fetch_tourism_monthly`][yohou.datasets._fetchers.fetch_tourism_monthly] : Monthly tourism series.
    - [`fetch_electricity_demand`][yohou.datasets._fetchers.fetch_electricity_demand] : Half-hourly electricity demand 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.4654722

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

    """
    return _fetch_dataset(
        metadata=SUNSPOT,
        dataset_name="sunspot",
        value_column_name="sunspot_number",
        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 Choose a Decomposition Strategy

  ---

  Forecasting-Models

  Build 2- and 3-component DecompositionPipeline forecasters chaining trend, seasonality, and residual models with target pre-transformation.

  View · Open in marimo