The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
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In this paper we propose a method for nonlinear time series pattern matching: "Multi-Scale Kernel Latent Variable (MSKLV) models". The pattern matching methodology includes multi-scale analysis using wavelet decomposition of time series and finding latent vectors in the kernel feature space at different scales of wavelet decomposition. Latent vectors so obtained are matched for similarity with the corresponding latent vectors obtained for time series in the historical database. The proposed methodology is applied on time series generated in the evolving stages of disturbances of Tennesse Eastman challenge problem and MSKLV models are found to be superior to Multi-scale Latent Variable (MSLV) models.