Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2003.820556
Title: Support vector machine with adaptive parameters in financial time series forecasting
Authors: Cao, L.J.
Tay, F.E.H. 
Keywords: Back-propagation (BP) neural network
Neural network
Nonstationarity
Regularized radial basis function (RBF)
Support vector machine (SVM)
Issue Date: Nov-2003
Source: Cao, L.J., Tay, F.E.H. (2003-11). Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks 14 (6) : 1506-1518. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2003.820556
Abstract: A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial forecasting.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/61420
ISSN: 10459227
DOI: 10.1109/TNN.2003.820556
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