API reference

Classes

ADAM([model, lags, ar_order, i_order, ...])	ADAM: Augmented Dynamic Adaptive Model for Time Series Forecasting.
AutoADAM([model, lags, ar_order, i_order, ...])	Automatic ADAM model selection.
ES([model, lags, persistence, phi, initial, ...])	Exponential Smoothing in Single Source of Error (SSOE) state space model.
MSARIMA([orders, lags, ar_order, i_order, ...])	Multiple Seasonal ARIMA in Single Source of Error state space form.
AutoMSARIMA([lags, ar_order, i_order, ...])	Automatic Multiple Seasonal ARIMA with order selection.
OM([model, lags, ar_order, i_order, ...])	Occurrence model — Python port of R's om().
OMG([model_a, model_b, lags, orders_a, ...])	General occurrence model — two parallel ETS sub-models combined.
AutoOM([model, lags, orders, occurrence, h, ...])	Automatic occurrence model selection — port of R's auto.om().

Functions

msdecompose(y[, lags, type, smoother])	Multiple seasonal decomposition of time series with multiple frequencies.
lowess(x[, y, f, iter, delta])	LOWESS smoother that exactly matches R's stats::lowess function.