Learning Engine


Forecast run: A forecast run is the process of creating forecast with a specific forecast setting.

Forecast method: A forecast method is an algorithm for creating forecast. It might have one parameter, several parameters, or no parameters.

Forecast setting: a forecast setting consists of a forecast method (e.g. exponential smoothing) its specific parameter values (e.g. alpha = 0.3 for a setting with exponential smoothing), and the number of past periods considered.

Heuristic Classification and Parameter Setting

A heuristic classification is implemented for historical sales data which have not been used for forecasting. It is investigated if the time series is intermittent. If not, then whether the time series has trend or seasonality. Based on the results, the parameters are set to values which give good results based on the forecasting literature.


When forecasting, several learning runs are processed, too. A learning run is a forecast with an experimental setting. If a method and/or some parameters are found which give better results than the actual setting, then the learning setting will be used.