Automatic model selection procedure 1 is used in forecast strategies 50, 51, 52, 53, 54 and 55.
See Forecast Strategies.
The system checks whether the historical data shows seasonal effects by determining the autocorrelation function (see below) and comparing it with a value Q, which is 0.3 in the standard system. You can change this limit in the /SAPAPO/SCM_FCSTPARA BAdI – Method PARAMETER_SET
Similarly the system checks for trend effects by carrying out the trend significance test (formula below).
Formula for Autocorrelation Coefficient
Formula for Trend Significance Test
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1. The sytem first tests for intermittent historical data by determining the number of periods that do not contain any data in the historical key figure. If this is larger than 66% of the total number of periods, the system automatically stops model selection and uses the Croston method.
2. The system conducts initialization for the specified model and the test model (for example, in forecast strategy 54, the specified model is seasonal and the test model is a trend model).
In order for initialization to take place, a sufficient number of historical values needs to be present in the system. This can be 2 seasons at the most for the seasonal test and 3 periods for the trend test
If not enough historical values are present in the system for initialization to take place, the model selection procedure is canceled, and a forecast is carried out on the basis of the specified model (in strategy 54, this would be a seasonal model); if the forecast strategy is one in which no model is specified (for example, strategy 51), a forecast is created using a constant model. The exception to this rule is strategy 53, which tests for both trend and seasonal models; if sufficient historical values exist to initialize a trend test but not a seasonal test, only a trend test is carried out.
3. In forecast strategies 50, 51, 53 and 54, a seasonal test is carried out:
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a. Any trend influences on the historical time series are removed.
b. An autocorrelation coefficient is calculated.
c. The coefficient is tested for significance.
4. In forecast strategies 50, 52, 53 and 55, a trend test is carried out.
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a. Any seasonal influences on the historical time series are removed.
b. A check parameter is calculated as in the formula above.
c. The system determines whether the historical data reveals a significant trend pattern by checking against a value that depends on the number of periods.
5. If neither the seasonal test nor the trend test is positive, the system uses the constant model (see Constant Model w. 1st Order Exponential Smoothing).
If the seasonal test is positive, the seasonal model is used with the specified parameters (see Trend/Seasonal Models w. 1st Order Exp. Smoothing).
If the trend test is positive, the trend model is used with 1st order exponentail smoothing and the specified paramters (see Trend/Seasonal Models w. 1st Order Exp. Smoothing).
If both tests are positive, the seasonal trend model is used with the specified parameters.
The Periods per Season value in the forecast profile is very important. For instance if your historical data has a season of 7 periods and you enter a Periods per Season value of 3, the seasonal test will probably be negative. No seasonal models are then tried; only trend and constant models.
Overview of Strategies that use Automatic Model Selection 1
Strategy 
Name 
Seasonal model 
Trend model 
Seasonal trend model 
Constant model 
51 
Test for trend 

+ 

O 
52 
Test for season 
+ 


O 
53 
Test for trend and season 
+ 
+ 
If both tests +ve 
O 
54 
Seasonal model and test for trend 
O 

+ 

55 
Trend model and test for seasonal 

O 
+ 

O – default method
+  method that is used if the test is positive