Model Initialization
Model initialization is the process of specifying the necessary model parameters, such as the basic value, the trend value and the seasonal indices for the selected forecast model. In each case, initialization occurs during the first forecast for a material. It must also be carried out in cases of structural interruption, that is, if the existing forecast becomes invalid.
Which model parameters are necessary for which forecast model is shown in the following Table:
Model Parameters
Model |
Model parameters |
Constant model |
basic value |
Trend model |
basic value, trend value |
Seasonal model |
basic value, seasonal indices |
Seasonal trend model |
basic value, trend value, seasonal indices |
The forecast model is usually automatically initialized. In order to do this, the system requires a certain amount of historical values. The number required varies depending on the forecast model as shown in the table below.
Number of Historical Values Required for Model Initialization
Model |
No. of historical values |
Constant model |
1 |
Trend model |
3 |
Seasonal model |
1 season |
Seasonal trend model |
1 season + 3 |
2nd-order exp. smoothing |
3 |
Moving average |
1 |
Weighted moving average |
1 |
The system calculates the basic value on the basis of an average value, and the trend using the results of the regression analysis. The seasonal indices result from the quotient of the actual past value and the basic value which has been adjusted for the trend value.
These calculation methods are used for the constant, trend, seasonal and seasonal trend models, depending on which parameters are to be determined.
For the second-order exponential smoothing model, a regression analysis is carried out.
For the moving average and the weighted moving average models, the system calculates an average value.