Research on stock index forecasting based on ARIMA-GARCH and SVM mixed model

Abstract

With the development of information technology, financial forecasting is the research direction of traditional mathematical statistics and combines emerging data mining technologies such as machine learning and deep learning to make forecasts. This paper starts from the traditional time series model , which can handle the conditional heteroscedasticity of time series data and combines the advantages of a support vector machine (SVM) that can handle the nonlinear part of financial time data . Stock market forecasts. In order to test the prediction effect of the model and compare traditional prediction methods with modern machine learning prediction methods, this paper establishes ARIMA-GARCH, SVM, and the combination ARIMA-GARCH- SVM model of these two types, respectively.