A Time Series Data Regression Model Based on Multi-kernel KPCA Dimension Reduction and Bagging Algorithms

Abstract

Regression models for high-dimensional data have always been a hot topic in the field of statistical learning. Considering the case that the predictor variable is a high-frequency time series and the response variable is a continuous scalar, this paper proposes a regression method based on a multi-kernel KPCA Dimension reduction method and the Bagging algorithms. The proposed method adaptively solves the problem of kernel function selection and unsupervised ness in KPCA Dimension reduction by splicing the projection data under various kernel functions and using the Bagging algorithms to mine the relationship between projection data and the response variable. In the real data analysis, this paper selects the multiple regression model, the LASSO regression based on model selection, the multiple regression model based on PCA Dimension reduction and single-kernel KPCA Dimension reduction as comparison methods. The results show that the proposed method has better performance than other comparison methods. Since the basic regressor in the Bagging framework is model-free, some prediction models can be used to improve the prediction accuracy for more complex data situations.