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module statsforecast.adapters.prophet

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class AutoARIMAProphet

AutoARIMAProphet adapter. Returns best ARIMA model using external variables created by the Prophet interface. This class receives as parameters the same as prophet.Prophet and uses a models.AutoARIMA backend. If your forecasting pipeline uses Prophet the AutoARIMAProphet adapter helps to easily substitute Prophet with an AutoARIMA. Args:
  • growth (str, default=“linear”): ‘linear’, ‘logistic’ or ‘flat’ to specify a linear, logistic or flat trend.
  • changepoints (List of dates, default=None): Potential changepoints. Otherwise selected automatically.
  • n_changepoints (int, default=25): Number of potential changepoints to include.
  • changepoint_range (float, default=0.8): Proportion of history in which trend changepoints will be estimated.
  • yearly_seasonality (str, bool or int, default=“auto”): Fit yearly seasonality. Can be ‘auto’, True, False, or a number of Fourier terms to generate.
  • weekly_seasonality (str, bool or int, default=“auto”): Fit weekly seasonality. Can be ‘auto’, True, False, or a number of Fourier terms to generate.
  • daily_seasonality (str, bool or int, default=“auto”): Fit daily seasonality. Can be ‘auto’, True, False, or a number of Fourier terms to generate.
  • holidays (pandas.DataFrame, default=None): DataFrame with columns holiday (string) and ds (date type).
  • interval_width (float, default=0.80): Uncertainty forecast intervals width. StatsForecast’s level
Note:
You can create automated exogenous variables from the Prophet data processing pipeline these exogenous will be included into AutoARIMA’s exogenous features. Parameters like seasonality_mode, seasonality_prior_scale, holidays_prior_scale, changepoint_prior_scale, mcmc_samples, uncertainty_samples, stan_backend are Prophet exclusive. References: Sean J. Taylor, Benjamin Letham (2017). “Prophet Forecasting at Scale” Oskar Triebe, Hansika Hewamalage, Polina Pilyugina, Nikolay Laptev, Christoph Bergmeir, Ram Rajagopal (2021). “NeuralProphet: Explainable Forecasting at Scale”. Rob J. Hyndman, Yeasmin Khandakar (2008). “Automatic Time Series Forecasting: The forecast package for R”.

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method __init__

__init__(
    growth='linear',
    changepoints=None,
    n_changepoints=25,
    changepoint_range=0.8,
    yearly_seasonality='auto',
    weekly_seasonality='auto',
    daily_seasonality='auto',
    holidays=None,
    seasonality_mode='additive',
    seasonality_prior_scale=10.0,
    holidays_prior_scale=10.0,
    changepoint_prior_scale=0.05,
    mcmc_samples=0,
    interval_width=0.8,
    uncertainty_samples=1000,
    stan_backend=None,
    d=None,
    D=None,
    max_p=5,
    max_q=5,
    max_P=2,
    max_Q=2,
    max_order=5,
    max_d=2,
    max_D=1,
    start_p=2,
    start_q=2,
    start_P=1,
    start_Q=1,
    stationary=False,
    seasonal=True,
    ic='aicc',
    stepwise=True,
    nmodels=94,
    trace=False,
    approximation=False,
    method=None,
    truncate=None,
    test='kpss',
    test_kwargs=None,
    seasonal_test='seas',
    seasonal_test_kwargs=None,
    allowdrift=False,
    allowmean=False,
    blambda=None,
    biasadj=False,
    period=1
)

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method fit

fit(df, disable_seasonal_features=True)
Fit the AutoARIMAProphet adapter. Args:
  • df (pandas.DataFrame): DataFrame with columns ds (date type) and y, the time series.
  • disable_seasonal_features (bool, default=True): Disable Prophet’s seasonal features.
Returns:
  • AutoARIMAProphet: Adapter object with AutoARIMA fitted model.

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method predict

predict(df=None)
Predict using the AutoARIMAProphet adapter. Args:
  • df (pandas.DataFrame, default=None): DataFrame with columns ds (date type) and y, the time series.
Returns:
  • pandas.DataFrame: DataFrame with the forecast components.