Use
Model initialization is the process of determining the necessary model parameters
¾ such as the basic value, the trend value, and the seasonal indices ¾ for the selected forecast model. Initialization takes place each time a planning time series is forecasted.Features
The following table shows you which model parameters are necessary for each forecast model.
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 |
As a general rule, the forecast model is initialized automatically. In order to do this, the system requires a certain number of historical values. This number depends on the forecast model, as shown in the following table.
Model |
Number of historical values |
Constant model |
1 |
Trend model |
3 |
Seasonal model |
1 season |
Seasonal trend model |
1 season + 3 |
2nd-order exponential smoothing |
3 |
Moving average |
1 |
Weighted moving average |
1 |
The system determines the basic value on the basis of the average and determines the trend using the regression analysis. The seasonal indices are given by the actual historical value divided by the basic value 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.
A regression analysis is carried out for the second-order exponential smoothing model.
For the moving average and weighted moving average models, the system calculates an average value.
See also:
Forecast Parameters