Adaptive weighted-function models for time series prediction

Time series prediction has been widely used in various fields. GEP is one of the popular methods for time series analysis. However, the GEP-based prediction models contain only one single function. To accurately capture the dynamic behavior of time series, this study develops a system which integrates multiple functions in a GEP-based model for time series prediction. The weight of each function is determined by the accuracy of its last prediction. In addition, a light local search is applied to adjust the function weights. The experimental results show that the proposed system outperforms several GEP-based approaches.