Situation Forecast Algorithm Based on Information Entropy

Aiming at the problem of multi-model forecast of situation, a situation combination forecast model based on information entropy is proposed. Firstly, the forecast sub-model is trained to obtain the precision error sequence of each sub-model. Then the forecast stability of the sub-model is evaluated based on the information entropy. Finally, the combination forecast model is constructed to complete the situation combination forecast. The MATLAB simulation results show that this method can automatically adjust based on the change characteristics of the situation curve, and is superior to the existing typical methods in stability and forecast accuracy.