Fuzzy logic, neural networks, and soft computing
Communications of the ACM
The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Applied Wavelet Analysis with S-Plus
Applied Wavelet Analysis with S-Plus
Walvelet Analysis of Commodity Price Behavior
Computational Economics
A Stochastic Nonlinear Regression Estimator Using Wavelets
Computational Economics
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A multiple-kernel support vector regression approach for stock market price forecasting
Expert Systems with Applications: An International Journal
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Multi-agent modeling of multiple FX-markets by neural networks
IEEE Transactions on Neural Networks
Multiresolution forecasting for futures trading using wavelet decompositions
IEEE Transactions on Neural Networks
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Abstract: Financial time series are nonlinear and non-stationary. Most financial phenomena cannot be clearly characterized in time domain. Therefore, traditional time domain models are not very effective in financial forecasting. To address the problem, this study combines wavelet analysis with kernel partial least square (PLS) regressions for stock index forecasting. Wavelet transformation maps time domain inputs to time-frequency (or wavelet) domain, where financial characteristics can be clearly identified. Because of the high dimensionality and heavy multi-collinearity of the input data, a wavelet domain kernel PLS regressor is employed to create the most efficient subspace that maintains maximum covariance between inputs and outputs, and to perform final forecasting. Empirical results demonstrate that the proposed model outperforms traditional neural networks, support vector machines, GARCH models, and has significantly reduced the forecasting errors.