Stock Index Prediction Method Based on Dynamic Weighted Ensemble Learning

Abstract

It is found that the prediction model has great influence on the performance of stock index. The traditional ensemble learning model has some problems such as limited use of high performance basic classifiers in stock index regression prediction. In this paper, it is found that there is a certain degree of complementarity between basic classifiers. In order to make use of the complementarity of different models, this paper proposes a dynamic weighted ensemble learning model for stock index prediction. The experimental results show that the dynamic weighted ensemble learning model is more accurate than the single basic classifier and is suitable for the regression prediction of different stock indexes.