论文信息 - Time series forecasting by hybrid artificial intelligence architecture and its application

Time series forecasting by hybrid artificial intelligence architecture and its application

This paper proposed a hybrid model to improve the single SVR model. The hybrid model that is composed of neural network and support vector machine (SVR) has a two-stage neural network architecture. In the first stage, self-organizing feature map (SOM) can be used as a clustering algorithm to partition the whole input space into several disjointed regions. In the second stage, based on the principle of least error, SVR which best fit partitioned regions are constructed by finding the most appropriate kernel function. The application of GDP, CPI and Total Foreign Trade Volume prediction shows that SOM-SVR models achieve significant improvements in the generalization performance compared with the single SVR model. Additionally, the SOM-SVR models also converge faster.

Yang Jian-hui | Li Long | Zhao Chen-hui

[1] Thomas F. Coleman,et al. Estimating a Hedge Fund Return Model Based on a Small Number of Samples , 2009, INFOR Inf. Syst. Oper. Res..

[2] Zhao Feng. Time Series Prediction Using Support Vector Regression Neural Networks , 2008 .

[3] Wei Wu,et al. Soil water content forecasting by ANN and SVM hybrid architecture , 2008, Environmental monitoring and assessment.

[4] F. Girosi,et al. Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[5] Wang Wei. Applying artificial neural network to the predicting of nonlinear economy , 2000 .

[7] Gunnar Rätsch,et al. Using support vector machines for time series prediction , 1999 .

[8] Kang-Lin Peng,et al. THE DEVELOPMENT OF A NEW STATISTICAL TECHNIQUE FOR RELATING FINANCIAL INFORMATION TO STOCK MARKET RETURNS , 2004 .

[9] Gunnar Rätsch,et al. Predicting Time Series with Support Vector Machines , 1997, ICANN.

[10] Lijuan Cao,et al. Support vector machines experts for time series forecasting , 2003, Neurocomputing.

[11] Xiao Jian-hua,et al. A SVR-based Model for Regional Economy Medium-term and Long-term Forecast , 2006 .

[12] Alexander Gammerman,et al. Hedging predictions in machine learning , 2006, ArXiv.