Support vector machine with adaptive parameters in financial time series forecasting
Support vector machine with adaptive parameters in financial time series forecasting
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DOI：
10.1109/tnn.2003.820556
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发表时间：
2003-11-01
影响因子：
--
通讯作者：
Tay, FEH
中科院分区：
文献类型：
--
作者：
Cao, LJ;Tay, FEH
支持向量机（Support Vector Machine，SVM）是一种新型的学习机，由于其出色的泛化性能，在模式识别和回归估计等领域受到越来越多的关注。研究了支持向量机在金融时间序列预测中的应用。首先通过与多层BP神经网络和正则化径向基函数神经网络的比较，验证了支持向量机在金融预测中应用的可行性。实验研究了支持向量机的性能相对于自由参数的变化。然后，通过将金融时间序列的非平稳性引入SVM，提出了自适应参数。五个真实的期货合约整理从芝加哥商品市场作为数据集。仿真结果表明，在这三种方法中，SVM的预测性能优于BP神经网络，且与正则化RBF神经网络的泛化性能相当。此外，支持向量机的自由参数对泛化性能有很大的影响。在金融预测中，参数自适应的支持向量机既可以获得更高的泛化性能，又可以使用比标准支持向量机更少的支持向量。
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.