International Journal of Computational Intelligence Systems
International Journal of Computational Intelligence Systems
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DOI：
10.1080/18756891.2013.864472
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发表时间：
2013-01
期刊：
影响因子：
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通讯作者：
Yi Xiao;Jin Xiao;Fengbin Lu;Shouyang Wang
中科院分区：
文献类型：
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作者：
Yi Xiao;Jin Xiao;Fengbin Lu;Shouyang Wang
股票网上交易价格预测是一个日益受到关注的重要金融问题。提出了一种新的三阶段非线性系综模型。在该模型中，三种不同类型的基于神经网络的模型，即Elman网络，广义回归神经网络（GRNN）和小波神经网络（WNN）是由三个不重叠的训练集，并进一步优化改进的粒子群优化（IPSO）。最后，通过支持向量机（SVM）神经网络学习三个基于神经网络的模型，生成一个基于神经网络的非线性元模型。所提出的方法的优越性在于它的灵活性，以考虑潜在的复杂的非线性关系。利用三个股指日序列对预测模型进行了验证。实证结果表明，集成ANNs-PSO-GA方法可以显着提高预测性能比其他个人模型和线性组合模型在这项研究中列出。
Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN) and wavelet neural network (WNN) are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO). Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM) neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.