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Forecasting algorithm using an improved genetic algorithm based on backpropagation neural network model
Journal of the Korean Data & Information Science Society 2017;28:1327-36
Published online November 30, 2017
© 2017 Korean Data & Information Science Society.

YeoChang Yoon1 · Na Rae Jo2 · Sung Duck Lee3

1Department of Information Security, Woosuk University
23Department of Information and Statistics, Chungbuk National University
Correspondence to: Sung Duck Lee
Professor, Department of Information and Statistics, Chungbuk National University, 1, Chungdae-ro, Seowon-gu, Cheongju-si, Chungcheongbuk-do, Korea. E-mail: sdlee@chungbuk.ack.kr
Received October 20, 2017; Revised November 15, 2017; Accepted November 21, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In this study, the problems in the short term stock market forecasting are analyzed and the feasibility of the ARIMA method and the backpropagation neural network is discussed. Neural network and genetic algorithm in short term stock forecasting is also examined. Since the backpropagation algorithm often falls into the local minima trap, we optimized the backpropagation neural network and established a genetic algorithm based on backpropagation neural network for forecasting model in order to achieve high forecasting accuracy. The experiments adopted the korea composite stock price index series to make prediction and provided corresponding error analysis. The results show that the genetic algorithm based on backpropagation neural network model proposed in this study has a significant improvement in stock price index series forecasting accuracy.
Keywords : Backpropagation, forecasting, GA-BP, genetic algorithm, initial weight