TSB

Teunter-Syntetos-Babai method for forecasting intermittent time series.

Quickstart

from sktime.forecasting.tsb import TSB

estimator = TSB(alpha=0.1, beta=0.1)

Parameters(2)

alphafloat, default = 0.1

Smoothing parameter for demand size (d)

betafloat, default = 0.1

Smoothing parameter for demand occurrence probability (p)

Examples

>>> from sktime.forecasting.tsb import TSB
>>> from sktime.datasets import load_PBS_dataset
>>> y = load_PBS_dataset ()
>>> forecaster = TSB (alpha = 0.4, beta = 0.05)
>>> forecaster. fit (y) TSB(
... )
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ])

References

1. [1 ] Teunter, R.H., Syntetos, A.A. and Zied Babai, M. (2011) Intermittent Demand: Linking Forecasting to Inventory Obsolescence. European Journal of Operational Research, 214, 606-615. https://nixtlaverse.nixtla.io/statsforecast/docs/models/tsb.html https://juanitorduz.github.io/tsb_numpyro/