Open AccessChaotic time series prediction based on multi-kernel learning support vector regression
Author(s) -
Junfeng Zhang,
HU Shou-song
Publication year - 2008
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.57.2708
Subject(s) - chaotic , kernel (algebra) , support vector machine , generalization , computer science , series (stratigraphy) , quadratic programming , quadratic equation , kernel method , artificial intelligence , time series , machine learning , algorithm , mathematical optimization , mathematics , mathematical analysis , paleontology , geometry , combinatorics , biology
Multi-kernel learning support vector regression (MKL-SVR) are proposed for chaotic time series prediction to solve the problems of kernel selection and hyper-parameter determination when using the standard SVR. The algorithm is realized through quadratic constrained quadratic programming (QCQP) in the hybrid kernel space, which not only reduces the number of support vectors, but also improves the prediction performance. Finally, it is applied to Mackey-Glass, Lorenz and Henon chaotic time series prediction. The results indicate that the proposed method can effectively increase the prediction precision, accelerate the convergency of cascade learning and enhance the generalization of prediction model.