Automatic Model Selection 
Automatic Model Selection
If you do not want to specify a forecast model manually, you must instruct the system to make an automatic selection. With automatic selection, the system analyzes the historical data and then selects the most suitable model. The following models are possible:
If the system cannot detect any regular pattern in the historical data, it automatically selects the constant model.
The system requires a different number of historical values for different tests. For further information, see
Model Selection Procedures
You choose between two procedures for automatic model selection:
Procedure 1
If you want the system to select the forecast model, you can choose between various statistical tests and test combinations which determine the model. The test that is carried out depends on your level of knowledge (see table below).
Trend test
In the trend test, the system subjects the historical values to a regression analysis and checks to see whether there is a significant trend pattern.
Seasonal test
In the seasonal test, the system clears the historical values of any possible trends and then carries out an auto-correlation test.
Pattern |
Test |
No information |
Test for trend and seasonal patterns |
No trend |
Test for seasonal pattern |
No season |
Test for trend pattern |
Trend |
Test for seasonal pattern |
Season |
Test for trend pattern |
If you know that a particular pattern exists or does not exist before the model is selected, you can have the system test the historical time series for a trend pattern or a seasonal pattern.
If you are unable to make any statement about the historical pattern, the system carries out a trend test and a seasonal test. The forecast model is determined on the basis of which test produces the more significant results (see table below).
Procedure 2
The system calculates the models to be tested using various combinations for alpha, beta, and gamma. The smoothing factors are also varied between 0.1 and 0.5 in intervals of 0.1. The system then chooses the model which displays the lowest mean absolute deviation (MAD). Procedure 2 is more precise than procedure 1, but takes much longer.
To use procedure 2 in mass processing, set forecast strategy 56 in the forecast profile. If you run the forecast online and set Automatic model selection in the Model Selection dialog box, a further dialog box appears in which you can set procedure 2 as one of your forecast parameters.