mojolearn.DampedETS#

class mojolearn.DampedETS(season_length=1, error='A', alpha=None, beta=None, phi=None, numeric_mode=None)#

Holt’s damped additive trend model, ETS(A,Ad,N).

ETS with model=error + "AN" and damped=True: a damped additive trend and no season. Set error="M" for multiplicative errors (strictly positive data).

Parameters:

  • season_length (int, default 1) – Number of observations per season (no season is fitted).

  • error ({“A”, “M”}, default “A”) – Additive or multiplicative errors.

  • alpha, beta, phi (float or None, default None) – Fix the level, trend and damping smoothing parameters; None estimates each.

  • numeric_mode (str or None, default None) – Numeric mode for this model ('identical' or 'fast'); None uses the process default (see mojolearn.set_numeric_mode()). 'fast' is available on Apple GPUs only.

Variables:

info_ (ndarray of shape (batch_size, 10), float32) – Per-series fit summary. Set by predict().

Examples

>>> import numpy as np, mojolearn
>>> y = (100 + np.cumsum(np.random.default_rng(0).normal(1, 2, 80))).astype(np.float32)
>>> mojolearn.DampedETS().fit(y).predict(10)["mean"].shape
(10,)

See also

User guide: Time series

Choosing an estimator

Methods

fit(y[, X])

Check and store one series or a batch of series.

forecast(y, h[, X, X_future, level, fitted])

Fit y and forecast h steps in one call.

predict(h[, X, level])

Fit the model and forecast h steps ahead.
fit(y, X=None)#

Check and store one series or a batch of series.

The model itself is estimated by predict().

Parameters:

  • y (array-like of shape (n_obs,) or (batch_size, n_obs)) – The series, float32. A private copy is kept.

  • X (None) – Exogenous regressors are not supported; anything other than None raises NotImplementedError.

Returns:

self

Raises:

  • TypeError – If y is float64.

  • ValueError – If a series has fewer than 4 observations, if a multiplicative model meets non-positive or non-finite values, or if there are too many parameters for the number of observations.

  • NotImplementedError – If n_obs is at most the parameter count plus 4.

forecast(y, h, X=None, X_future=None, level=None, fitted=False)#

Fit y and forecast h steps in one call.

Equivalent to fit(y).predict(h). fitted=True (in-sample fitted values) raises NotImplementedError.

Returns:

dict – {"mean": forecasts}, as returned by predict().

predict(h, X=None, level=None)#

Fit the model and forecast h steps ahead.

Parameters:

  • h (int) – Forecast horizon.

  • X (None) – Unused.

  • level (None) – Prediction intervals are not supported; anything other than None raises NotImplementedError.

Returns:

dict – {"mean": forecasts}, shaped (h,) for a single series or (batch_size, h) for a batch.