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Forecaster

TSB

API referenceSource
Categorical in XInsamplePred int insampleExogenous

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

Implements the method proposed by Teunter, Syntetos, Babai in [1].

The TSB method is a modification of Croston’s method to handle intermittent time series with very sporadic demand, providing a more accurate estimation of the risk of obsolescence. Instead of forecasting the demand interval, TSB forecasts demand probability which is updated every period.

TSB will predict a constant value for all future times, so it essentially provides another notion for the average value of a time series.

Quickstart

python
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]

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.