Iterated time series prediction with multiple support vector regression models
Tài liệu tham khảo
Vapnik, 1995
Burges, 1998, A tutorial on support vector machines for pattern recognition, Data Min. Knowl. Discovery, 2, 121, 10.1023/A:1009715923555
Smola, 2004, A tutorial on support vector regression, Stat. Comput., 14, 199, 10.1023/B:STCO.0000035301.49549.88
Barreto, 2003, A taxonomy for spatiotemporal connectionist networks revisited, Neural Comput., 15, 1255, 10.1162/089976603321780281
A. Bouchachia, S. Bouchachia, Ensemble learning for time series prediction, in: First International Workshop on Nonlinear Dynamics and Synchronization, 2008.
Zhang, 1998, Forecasting with artificial neural networks, Int. J. Forecast., 14, 35, 10.1016/S0169-2070(97)00044-7
Cowper, 2002, Nonlinear prediction of chaotic signals using a normalized radial basis function network, Signal Process., 82, 775, 10.1016/S0165-1684(02)00155-X
Parlos, 2000, Multi-step-ahead prediction using dynamic recurrent neural networks, Neural Networks, 13, 765, 10.1016/S0893-6080(00)00048-4
E.A. Wan, Time series prediction by using a connectionist network with internal delay lines, in: Proceedings of NATO Advanced Research Workshop Comparative Time Series Analysis, Addison-Wesley, 1994, pp. 195–217.
Chang, 2007, Multi-step-ahead neural networks for flood forecasting, Hydrol. Sci. J., 52, 114, 10.1623/hysj.52.1.114
Bontempi, 1999, Local learning for iterated time-series prediction, 32
Taieb, 2010, Multiple-output modeling for multi-step-ahead time series forecasting, Neurocompting, 73, 1950, 10.1016/j.neucom.2009.11.030
A. Girard, C.E. Rasmussen, J.Q. nonero Candela, R. Murray-Smith, Gaussian process priors with uncertain inputs—application to multiple-step ahead time series forecasting, in: Advances in Neural Information Processing Systems, vol. 15, Vancouver, Canada, 2002, pp. 529–536.
K.-R. Müller, A. J. Smola, G. Ratsch, B. Schölkopf, J. Kohlmorgen, V.N. Vapnik, Predicting time series with support vector machines, in: Proceedings of 7th International Conference Artificial Neural Networks, vol. 1327, Lausanne, Switzerland, 1997, pp. 999–1004.
Kim, 2003, Financial time series forecasting using support vector machines, Neurocomputing, 55, 307, 10.1016/S0925-2312(03)00372-2
Cao, 2003, Support vector machine with adaptive parameters in financial time series forecasting, IEEE Trans. Neural Networks, 14, 1506, 10.1109/TNN.2003.820556
L. Zhang, Y.G. Xi, Nonlinear system identification based on an improved support vector regression estimator, in: International Symposium on Neural Networks, Dalian, China, 2004, pp. 586–591.
Shi, 2007, Support vector echo-state machine for chaotic time-series prediction, IEEE Trans. Neural Networks, 18, 359, 10.1109/TNN.2006.885113
Yang, 2009, Localized support vector regression for time series prediction, Neurocomputing, 72, 2659, 10.1016/j.neucom.2008.09.014
He, 2008, Model optimizing and feature selecting for support vector regression in time series forecasting, Neurocomputing, 72, 600, 10.1016/j.neucom.2007.11.010
Zhang, 2009, Identification and control of discrete-time nonlinear systems using affine support vector machines, Int. J. Artif. Intell. Tools, 18, 929, 10.1142/S0218213009000469
Cao, 2003, Support vector machines experts for time series forecasting, Neurocomputing, 51, 321, 10.1016/S0925-2312(02)00577-5
Wang, 2008, Online prediction model based on support vector machine, Neurocomputing, 71, 550, 10.1016/j.neucom.2007.07.020
Lau, 2008, Local prediction of non-linear time series using support vector regression, Pattern Recognition, 41, 1539, 10.1016/j.patcog.2007.08.013
D.M. Kline, Methods for multi-step time series forecasting neural networks, in: Neural Networks for Business Forecasting, Idea Group Inc. Global Publishing, 2004, pp. 226–250 (Chapter XII).
Sorjamaa, 2007, Methodology for long-term prediction of time series, Neurocomputing, 70, 2861, 10.1016/j.neucom.2006.06.015
Grassberger, 1983, Estimation of the Kolmogorov entropy from a chaotic signal, Phys. Rev. A, 28, 2591, 10.1103/PhysRevA.28.2591
Liebert, 1989, Proper choice of the time delay for the analysis of chaotic time series, Phys. Rev. A, 142, 107
C. Saunders, M.O. Stitson, J. Weston, L. Bottou, B. Schölkopf, A. Smola, Support Vector Machine—Reference Manual, Technical Report CSD-TR-98-03, Department of Computer Science, Royal Holloway, University of London, Egham, UK, 1998.
Zhang, 2004, Wavelet support vector machine, IEEE Trans. Syst. Man Cybern. Part B, 34, 34, 10.1109/TSMCB.2003.811113
Zhang, 2005, Support vector machines based on the orthogonal projection kernel of father wavelet, Int. J. Comput. Intell. Appl., 5, 283, 10.1142/S1469026805001489
C.C. Chang, C.J. Lin, LIBSVM: A Library for Support Vector Machines, 〈http://www.csie.ntu.edu.tw/∼cjlin/libsvm〉, 2001.
Tong, 1990
R. Hyndman, Time Series Data Library 〈http://robjhyndman.com/TSDL/physics/SUNSPOT.DAT〉, 2011.
Weigend, 1994