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module datasetsforecast.m4

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class Yearly

Yearly(seasonality: int = 1, horizon: int = 6, freq: str = ‘Y’, name: str = ‘Yearly’, n_ts: int = 23000)

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method __init__

__init__(
    seasonality: int = 1,
    horizon: int = 6,
    freq: str = 'Y',
    name: str = 'Yearly',
    n_ts: int = 23000
) → None

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class Quarterly

Quarterly(seasonality: int = 4, horizon: int = 8, freq: str = ‘Q’, name: str = ‘Quarterly’, n_ts: int = 24000)

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method __init__

__init__(
    seasonality: int = 4,
    horizon: int = 8,
    freq: str = 'Q',
    name: str = 'Quarterly',
    n_ts: int = 24000
) → None

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class Monthly

Monthly(seasonality: int = 12, horizon: int = 18, freq: str = ‘M’, name: str = ‘Monthly’, n_ts: int = 48000)

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method __init__

__init__(
    seasonality: int = 12,
    horizon: int = 18,
    freq: str = 'M',
    name: str = 'Monthly',
    n_ts: int = 48000
) → None

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class Weekly

Weekly(seasonality: int = 1, horizon: int = 13, freq: str = ‘W’, name: str = ‘Weekly’, n_ts: int = 359)

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method __init__

__init__(
    seasonality: int = 1,
    horizon: int = 13,
    freq: str = 'W',
    name: str = 'Weekly',
    n_ts: int = 359
) → None

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class Daily

Daily(seasonality: int = 1, horizon: int = 14, freq: str = ‘D’, name: str = ‘Daily’, n_ts: int = 4227)

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method __init__

__init__(
    seasonality: int = 1,
    horizon: int = 14,
    freq: str = 'D',
    name: str = 'Daily',
    n_ts: int = 4227
) → None

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class Hourly

Hourly(seasonality: int = 24, horizon: int = 48, freq: str = ‘H’, name: str = ‘Hourly’, n_ts: int = 414)

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method __init__

__init__(
    seasonality: int = 24,
    horizon: int = 48,
    freq: str = 'H',
    name: str = 'Hourly',
    n_ts: int = 414
) → None

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class Other

Other(seasonality: int = 1, horizon: int = 8, freq: str = ‘D’, name: str = ‘Other’, n_ts: int = 5000, included_groups: Tuple = (‘Weekly’, ‘Daily’, ‘Hourly’))

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method __init__

__init__(
    seasonality: int = 1,
    horizon: int = 8,
    freq: str = 'D',
    name: str = 'Other',
    n_ts: int = 5000,
    included_groups: Tuple = ('Weekly', 'Daily', 'Hourly')
) → None

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class M4

M4(source_url: str = ‘https://raw.githubusercontent.com/Mcompetitions/M4-methods/master/Dataset/’, naive2_forecast_url: str = ‘https://github.com/Nixtla/m4-forecasts/raw/master/forecasts/submission-Naive2.zip’)

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method __init__

__init__(
    source_url: str = 'https://raw.githubusercontent.com/Mcompetitions/M4-methods/master/Dataset/',
    naive2_forecast_url: str = 'https://github.com/Nixtla/m4-forecasts/raw/master/forecasts/submission-Naive2.zip'
) → None

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method async_download

async_download(directory: str, group: Optional[str] = None) → None
Download M4 Dataset. Args:
  • directory (str): Directory path to download dataset.
Example: 
group = 'Hourly'
await M4.async_download('data', group=group)
df, *_ = M4.load(directory='data', group=group)
n_series = len(np.unique(df.unique_id.values))
display_str  = f'Group: {group} '
display_str += f'n_series: {n_series}'
print(display_str)

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method download

download(directory: str, group: Optional[str] = None) → None
Download M4 Dataset. Args:
  • directory (str): Directory path to download dataset.
  • group (str, optional): Name of the group to download. If None, downloads all. Defaults to None.

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method load

load(
    directory: str,
    group: str,
    cache: bool = True
) → Tuple[DataFrame, Optional[DataFrame], Optional[DataFrame]]
Downloads and loads M4 data. Args:
  • directory (str): Directory where data will be downloaded.
  • group (str): Group name.
  • Allowed groups: ‘Yearly’, ‘Quarterly’, ‘Monthly’, ‘Weekly’, ‘Daily’, ‘Hourly’.
  • cache (bool): If True saves and loads
Returns: Tuple[pd.DataFrame, Optional[pd.DataFrame], Optional[pd.DataFrame]]: Target time series with columns [‘unique_id’, ‘ds’, ‘y’], Static exogenous variables with columns [‘unique_id’, ‘ds’], and static variables.

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class M4Evaluation

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method evaluate

evaluate(directory: str, group: str, y_hat: Union[ndarray, str]) → DataFrame
Evaluates y_hat according to M4 methodology. Args:
  • directory (str): Directory where data will be downloaded.
  • group (str): Group name.
  • Allowed groups: ‘Yearly’, ‘Quarterly’, ‘Monthly’, ‘Weekly’, ‘Daily’, ‘Hourly’.
  • y_hat (Union[np.ndarray, str]): Group forecasts as numpy array or benchmark url from
  • https: //github.com/Nixtla/m4-forecasts/tree/master/forecasts.
Returns:
  • pd.DataFrame: DataFrame with columns OWA, SMAPE, MASE and group as index.
Examples: 
esrnn_url = 'https://github.com/Nixtla/m4-forecasts/raw/master/forecasts/submission-118.zip'
esrnn_evaluation = M4Evaluation.evaluate('data', 'Hourly', esrnn_url)

fforma_url = 'https://github.com/Nixtla/m4-forecasts/raw/master/forecasts/submission-245.zip'
fforma_forecasts = M4Evaluation.load_benchmark('data', 'Hourly', fforma_url)
fforma_evaluation = M4Evaluation.evaluate('data', 'Hourly', fforma_forecasts)

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method load_benchmark

load_benchmark(
    directory: str,
    group: str,
    source_url: Optional[str] = None
) → ndarray
Downloads and loads a bechmark forecasts. Args:
  • directory (str): Directory where data will be downloaded.
  • group (str): Group name.
  • Allowed groups: ‘Yearly’, ‘Quarterly’, ‘Monthly’, ‘Weekly’, ‘Daily’, ‘Hourly’.
  • source_url (str, optional): Optional benchmark url obtained from
  • https: //github.com/Nixtla/m4-forecasts/tree/master/forecasts. If None returns Naive2.
Returns:
  • np.ndarray: Numpy array of shape (n_series, horizon).