In this notebook we provide a NumPyro implementation of the TSB (Teunter, Syntetos and Babai) method for forecasting intermittent time series. The TSB method is similar to the Croston’s method in the sense that is constructs two different time series out of the original one and then forecast each of them separately, so that the final forecast is generated by combining the forecasts of the two time series. The main difference between the two methods is that the TSB method uses the demand probability instead of the demand periods. Consequently, let \(y_{t}\) denote the input time series then the TSB method is specified by the following equations:
Dr. Juan Camilo Orduz · Feb 17, 2024
TSB Method for Intermittent Time Series Forecasting in NumPyro
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In this notebook we provide a NumPyro implementation of the TSB (Teunter, Syntetos and Babai) method for forecasting intermittent time series. The TSB method is similar to the Croston’s method in the sense that is constructs two different time series out of the original one and then forecast each of them separately, so that the final forecast is generated by combining the forecasts of the two time…
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