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• W250522885 abstract "This study presents the time-divided forecasting model to integrate evolutionary optimization algorithm and change point detection based on artificial neural networks (ANN) for the prediction of (Korea) stock price index. The genetic algorithm(GA) is introduced as an evolutionary optimization method in this study. The basic concept of the proposed model is to obtain intervals divided by change points, to identify them as optimal or near-optimal change point groups, and to use them in the forecasting of the stock price index. The proposed model consists of three phases. The first phase detects successive change points. The second phase detects the change-point groups with the GA. Finally, the third phase forecasts the output with ANN using the GA. This study examines the predictability of the proposed model for the prediction of stock price index." @default.
• W250522885 created "2016-06-24" @default.
• W250522885 creator A5018753326 @default.
• W250522885 date "2003-04-01" @default.
• W250522885 modified "2023-10-18" @default.
• W250522885 title "Using Evolutionary Optimization to Support Artificial Neural Networks for Time-Divided Forecasting: Application to Korea Stock Price Index" @default.
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• W250522885 doi "https://doi.org/10.5351/ckss.2003.10.1.153" @default.
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