A Hybrid ARIMA-GA-SVR Model for Stock Price Forecasting

Abstract

Autoregressive integrated moving average (ARIMA) is a widely used linear model withgreat performance for time series forecasting problems. Supplemented by support vector regression (SVR), an effective method to solve the nonlinear problem with a kernel function, ARIMA-SVR model captures both linear and nonlinear patterns in stock price forecasting. However, it does not have high accuracy and parameter selection speed when its parameters are chosen by the traditional method. Therefore, in this study, we applied genetic algorithm (GA) to optimize the parameter selection process of SVR to improve the performance of the ARIMA-SVR model. Subsequently, we built the ARIMA-GA-SVR model by integrating ARIMA with optimized SVR. Finally, we used actual stock price data to compare the forecasting accuracy of the proposed model, ARIMA and ARIMA-SVR models using error functions. The result shows that the proposed ARIMA-GA-SVR model outperforms other models.