Browse > Article
http://dx.doi.org/10.7465/jkdi.2017.28.6.1327

Forecasting algorithm using an improved genetic algorithm based on backpropagation neural network model  

Yoon, YeoChang (Department of Information Security, Woosuk University)
Jo, Na Rae (Department of Information and Statistics, Chungbuk National University)
Lee, Sung Duck (Department of Information and Statistics, Chungbuk National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.28, no.6, 2017 , pp. 1327-1336 More about this Journal
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;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Yoon, Y. and Lee, S. (2013). A comparison of the effects of optimization learning rates using a modified learning process for generalized neural network. The Korean Journal of Applied Statistics, 26, 847-856.   DOI
2 Wu, Y. and Zhang, L. (2002). The effect of initial weight, learning rate and regularization on generalization performance and efficiency. Proceedings on ICSP, 1191-1194.
3 Xiaodong, Y. (2015). Selection of initial weights and thresholds based on the genetic algorithm with the optimized back-propagation neural network. 12th International Conference on Fuzzy Systems and Knowledge Discovery, 173-177.
4 Smith, M. (1993). Neural networks for statistical modeling, Van Nostrand Reinhold, New York.
5 Araujo, R. A. (2010). A hybrid intelligent morphological approach for stock market forecasting. Neural Processing Letters, 31, 195-217.   DOI
6 Box, G. E. P. and Jenkins, G. (1970). Time series analysis forecasting and control, Holdel-Day, San Francisco.
7 Demuth, H. and Beale, M. (2001). Neural network toolbox for use with MATLAB, TheMathWorks.
8 Hwang, S. Y. (2014). Contemporary review on the bifurcating autoregressive models : Overview and perspectives. Journal of the Korean Data & Information Science Society, 25, 1137-1149.   DOI
9 Jung, J. and Lee. S. (2016). Comparison study of SARIMA and ARGO models for influenza epidemics prediction. Journal of the Korean Data & Information Science Society, 27, 1075-1081.   DOI
10 Luo, B., Chen, Y. and Jiang, W. (2016). Stock market forecasting algorithm based on improved neural network. 2016 Eighth International Conference on Measuring Technology and Mechatronics Automation, 628-631.
11 Song, J. (2016). A study on demand forecasting for Jeju-bound tourists by travel purpose using seasonal ARIMA-intervention model. Journal of the Korean Data & Information Science Society, 27, 725-732.   DOI
12 Yang, C. X. and Zhu, Y. F. (2007). Time series analysis using GA optimized neural networks. Third International Conference on Neural Computation, 270-276.
13 Yoon, Y. (2008). A learning using GA optimized neural networks. Proceedings of KIPS, 15, 27-29.
14 Yoon, Y. (2010). Time series forecasting based on genetic neural network. Proceedings of KIPS, 17, 1106-1108.