Using support vector machines for time series prediction

Chemometrics and Intelligent Laboratory Systems - Tập 69 - Trang 35-49 - 2003
U Thissen1, R van Brakel1, A.P de Weijer2, W.J Melssen1, L.M.C Buydens1
1Laboratory of Analytical Chemistry, University of Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The Netherlands
2Teijin Twaron Research Institute, Postbus 9600, 6800 TC Arnhem, The Netherlands

Tài liệu tham khảo

Rius, 1998, Reliability of analytical systems: use of control charts, time series models and recurrent neural networks (RNN), Chemometrics and Intelligent Laboratory Systems, 40, 1, 10.1016/S0169-7439(97)00085-3 Burges, 1998, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2, 121, 10.1023/A:1009715923555 A.J. Smola, B. Schölkopf, A tutorial on support vector regression, NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK, 1998. Available on http://www.kernel-machines.org/ Müller, 1997, Predicting time series with support vector machines, 999 Belousov, 2002, A flexible classification approach with optimal generalisation performance: support vector machines, Chemometrics and Intelligent Laboratory Systems, 64, 15, 10.1016/S0169-7439(02)00046-1 Cristianini, 2000 Campbell, 2002, Kernel methods: a survey of current techniques, Neurocomputing, 48, 63, 10.1016/S0925-2312(01)00643-9 Schölkopf, 2002 Suykens, 2002 Krogh, 1995, A simple weight decay can improve generalization, vol. 4, 950 Box, 1976 Chatfield, 1980 Mandic, 2001 Mackey, 1977, Oscillation and chaos in physiological control systems, Science, 197, 287, 10.1126/science.267326 S.R. Gunn, Support vector machines for classification and regression, Technical report, Image speech and intelligent systems research group, University of Southampton, UK, 1997. Available on http://www.isis.ecs.soton.ac.uk/isystems/kernel/. Mattera, 1999, Support vector machines for dynamic reconstruction of a chaotic system, 243 Platt, 1999, Flat training of support vector machines using sequential minimal optimisation, 185 Williams, 2001, Using the Nyström method to speed up kernel machines, vol. 13, 682 Fine, 2001, Efficient SVM training using low-rank kernel representation, Journal of Machine Learning Research, 2, 243 Joachims, 1999, Making large-scale support vector machines learning practical, 169 Suykens, 1999, Least squares support vector machines, Neural Processing Letters, 9, 293, 10.1023/A:1018628609742