mojolearn.CrostonSBA

class mojolearn.CrostonSBA(alias=None, numeric_mode=None)

Syntetos-Boylan approximation (SBA) of Croston’s method.

The CrostonClassic forecast multiplied by 0.95, which corrects Croston’s bias. Follows statsforecast’s CrostonSBA. fit takes one series or a batch of shape (batch_size, n_obs).

Parameters:

• alias (str or None, default None) – A display name for the model, stored as alias.

• 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:

mean_ (ndarray of shape (batch_size,), float32) – The fitted demand rate of each series; the point forecast.

Notes

Input must be float32; float64 raises TypeError. Prediction intervals, fitted values and exogenous regressors are not supported and raise NotImplementedError.

Examples

>>> import numpy as np, mojolearn
>>> rng = np.random.default_rng(0)
>>> y = (rng.random(120) < 0.3).astype(np.float32) * rng.integers(1, 10, 120).astype(np.float32)
>>> mojolearn.CrostonSBA().fit(y).predict(12)["mean"].shape
(12,)

See also

CrostonClassic, CrostonOptimized

User guide: Time series

Choosing an estimator

Methods

fit(y[, X])	Fit 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])	Forecast h steps ahead.

fit(y, X=None)

Fit one series or a batch of series.

Parameters:

• y (array-like of shape (n_obs,) or (batch_size, n_obs)) – The demand series, float32. One row per series for a batch.

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

Returns:

self – The fitted model, with mean_ set.

Raises:

• TypeError – If y is float64.

• ValueError – If a series has no observations.

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)

Forecast h steps ahead.

The Croston forecast is flat: each series’ fitted demand rate, repeated h times.

Parameters:

• h (int) – Forecast horizon, h >= 0.

• 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.