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 investor’s decisions and trades. In addition, in a dynamic environment such as the stock market, the non-linearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this work 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 proposed approach exploits a sliding-window metaheuristic-optimized machine learning regression technique. To illustrate the approach as well as to train and test it, it is applied to historical data of eight stock indices over six years from 2011 to 2017. The performance of the system was evaluated by calculating Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Square Error (MSE), Correlation Coefficient (R) and Non-linear Regression Multiple Correlation Coefficient (R2). The proposed hybrid prediction model exhibited outstanding prediction performance and it improves overall profit for investment performance. The proposed model is a promising predictive technique for highly non-linear time series, whose patterns are difficult to capture by traditional models.

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 investor’s decisions and trades. In addition, in a dynamic environment such as the stock market, the non-linearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this work 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 proposed approach exploits a sliding-window metaheuristic-optimized machine learning regression technique. To illustrate the approach as well as to train and test it, it is applied to historical data of eight stock indices over six years from 2011 to 2017. The performance of the system was evaluated by calculating Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Square Error (MSE), Correlation Coefficient (R) and Non-linear Regression Multiple Correlation Coefficient (R2). The proposed hybrid prediction model exhibited outstanding prediction performance and it improves overall profit for investment performance. The proposed model is a promising predictive technique for highly non-linear time series, whose patterns are difficult to capture by traditional models.

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