Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting

Authors:
F. Sun;C. Liang;L. Zhang;P. Lee;Z. Lin;J. Cao
Affiliations:
College of Computer Science and Technology, Jilin University, 10 Qian Wei Road, 130012, Changchun, China;College of Comp. Sci. and Tech., Jilin Univ., Changchun and Key Lab. of Intel. Info. Proc., Inst. of Comp. Tech., Chinese Acad. Sci., Beijing, China and Inst. of High Perf. Comp., Singapore Scienc ...;Institute of Computing Technology, Chinese Academy of Sciences, 1 Science Park Road, #01-01 The Capricorn, Singapore Science Park II, 100080, Beijing, China;Institute of High Performance Computing, 1 Science Park Road, #01-01 The Capricorn, Singapore Science Park II, 117528, Singapore, Singapore;Institute of High Performance Computing, 1 Science Park Road, #01-01 The Capricorn, Singapore Science Park II, 117528, Singapore, Singapore;Institute of High Performance Computing, 1 Science Park Road, #01-01 The Capricorn, Singapore Science Park II, 117528, Singapore, Singapore
Venue:
Neural Computing and Applications
Year:
2005

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Abstract

A novel neural-network-based method of time series forecasting is presented in this paper. The method combines the optimal partition algorithm (OPA) with the radial basis function (RBF) neural network. OPA for ordered samples is used to perform the clustering for the samples. The centers and widths of the RBF neural network are determined based on the clustering. The difference of the objective functions of the clustering is used to adjust the structure of the neural network dynamically. Thus, the number of the hidden nodes is selected adaptively. The method is applied to stock price prediction. The results of numerical simulations demonstrate the effectiveness of the method. Comparisons with the hard c-means (HCM) algorithm show that the proposed OPA method possesses obvious advantages in the precision of forecasting, generalization, and forecasting trends. Simulations also show that the OPA–orthogonal least squares (OPA–OLS) algorithm, which combines OPA with the OLS algorithm, results in better performance for forecasting trends.