If you do not want to specify a forecast model manually, you must instruct the system to make an automatic selection. The system then analyzes the historical data for different patterns and selects the appropriate model. The following models are possible:
- Constant model
- Trend model
- Seasonal model
- Seasonal trend model
If the system does not detect any regular pattern in the past consumption data, it automatically selects the constant model.
The system requires a different amount of historical values for the individual tests. For more information on the exact number that it requires, see Model Initialization.
Model Selection Procedures
If the system is to make an automatic model selection, you have the choice between two model selection procedures:
Procedure 1
The system carries out statistical tests and checks whether a trend or a seasonal requirements pattern applies.
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.
In the seasonal test, the system clears the historical values of any possible trends and carries out an autocorrelation test.
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 0.2 to 0.8 in intervals of 0.2.
The model which is then chosen is the the model which displays the lowest mean absolute deviation (MAD).
Procedure 2 is more precise than procedure 1 but takes considerably longer.
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