Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression

Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investors' decisions and trades. In addition, in a dynamic environment such as the stock market, the nonlinearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this paper proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices of Taiwan construction companies one step ahead. It may be of great interest to home brokers who do not possess sufficient knowledge to invest in such companies. The system has a graphical user interface and functions as a stand-alone application. The developed hybrid system exhibited outstanding prediction performance and it improves overall profit for investment performance. The proposed model is a promising predictive technique for highly nonlinear time series, whose patterns are difficult to capture by traditional models.