References
- Araujo, R. A. (2010). A hybrid intelligent morphological approach for stock market forecasting. Neural Processing Letters, 31, 195-217. https://doi.org/10.1007/s11063-010-9133-1
- Box, G. E. P. and Jenkins, G. (1970). Time series analysis forecasting and control, Holdel-Day, San Francisco.
- Demuth, H. and Beale, M. (2001). Neural network toolbox for use with MATLAB, TheMathWorks.
- 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. https://doi.org/10.7465/jkdi.2014.25.5.1137
- 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. https://doi.org/10.7465/jkdi.2016.27.4.1075
- 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.
- Smith, M. (1993). Neural networks for statistical modeling, Van Nostrand Reinhold, New York.
- 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. https://doi.org/10.7465/jkdi.2016.27.3.725
- Yang, C. X. and Zhu, Y. F. (2007). Time series analysis using GA optimized neural networks. Third International Conference on Neural Computation, 270-276.
- Yoon, Y. (2008). A learning using GA optimized neural networks. Proceedings of KIPS, 15, 27-29.
- Yoon, Y. (2010). Time series forecasting based on genetic neural network. Proceedings of KIPS, 17, 1106-1108.
- 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. https://doi.org/10.5351/KJAS.2013.26.5.847
- 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.
- 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.