A Wavelet Neural Network Forecasting Model Based on ARIMA

Abstract:

Stock index series is Non-stationary, Nonlinear and factors with impact on stock index fluctuation are complex, a time series forecasting model combined ARIMA model and wavelet neural network is presented. The combined model uses BP neural network as the main framework, uses wavelet basis function instead of transfer function in the network, also add some inner factors of the time series mining by ARIMA model, as the part impute of Wavelet Neural Network. So it is more scientific and rational that using inner factors and external other factors. The last simulate experiment shows that the wavelet neural network forecasting model based on ARIMA has higher accuracy than ARIMA model or BP network.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 3013-3018
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.3013
• Online since: August 2013
• Authors: Bin Wang, Wen Ning Hao, Gang Chen, Deng Chao He, Bo Feng
• Keywords: ARIMA Model, BP Neural Network (BPNN), Time Series, Wavelet Neural Network (WNN)

References

[1] CaiFeng, ZengFeng-zhang. ANN-ARMA Model for Forecasting Product Consumption Based on Non-Stationary Time Series, Transactions of Beijing institute of technology, 2007, 27(3).

[2] Xie Bing, Dai Sheng, XieKe-fan. Forecast of Industrial Stock Index Based on Neural Network and Genetic Algorithm, Hubei University. 2004, 11.

[3] Wu Ming-feng, Jiang Xun. Forecasting Model Based on Fuzzy Time Series, Value engineering, (2008).

[4] Chen Hai-ming, Li Dong, The application of grey forecast model in stock price, Sciences research management, 203, (3): 70-74.

[5] Watanbe H, Chakraborty, B. Chakrabortyj, Approach for Stock Price Trend Forecasting from Multivariate Time Series, Second International Conference on Information and Control, 2007 (9).

DOI: 10.1109/icicic.2007.527

[6] Huang, Fu-Yuan. Integration of an Improved Particle Swarm Algorithm and Fuzzy Neural Network for Shanghai stock Market Prediction, International Conference on Power Electronics and Intelligent Transportation System, 2008(8): 242-247.

DOI: 10.1109/peits.2008.85

[7] H. Nakayama, A. Hattori. Incremental learning and forgetting in RBF networks and SVM Witn applications to financial problems, Knowledge-based intelligent information and engineering systems, 2003, 2773(1): 1109-1115.

DOI: 10.1007/978-3-540-45224-9_149

[8] Pai,C. Lin. A hybrid ARIMA and support vector machines model in stock price forecasting, Omega, 2005, 33(6): 497-505.

DOI: 10.1016/j.omega.2004.07.024

[9] Shen Wei. Research on Method and Application of Building Forecasting Models for Stock Index Fluctuation , 2011, 6.

[10] Li Zhi. Wavelet theory and economic and financial sequential application research, (2007).