Dynamic support vector machines for non-stationary time series forecasting
暂无分享,去创建一个
Lijuan Cao | Qingming Gu | Lijuan Cao | Q. Gu
[1] Francis Eng Hock Tay,et al. Improved financial time series forecasting by combining Support Vector Machines with self-organizing feature map , 2001, Intell. Data Anal..
[2] J. Mercer. Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations , 1909 .
[3] Isabelle Guyon,et al. Automatic Capacity Tuning of Very Large VC-Dimension Classifiers , 1992, NIPS.
[4] W. G. Gilchrist,et al. Methods of Estimation Involving Discounting , 1967 .
[5] Shouhong Wang,et al. An Adaptive Approach to Market Development Forecasting , 1999, Neural Computing & Applications.
[6] Timo Teräsvirta,et al. Forecasting the consumption of alcoholic beverages in Finland: A box-Jenkins approach , 1976 .
[7] Vladimir Vapnik,et al. The Support Vector Method , 1997, ICANN.
[8] Andreas S. Weigend,et al. Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .
[9] Bernhard Schölkopf,et al. Learning with kernels , 2001 .
[10] Marie Cottrell,et al. Neural modeling for time series: A statistical stepwise method for weight elimination , 1995, IEEE Trans. Neural Networks.
[11] David Horn,et al. Learning the Rule of a Time Series , 1992, Int. J. Neural Syst..
[12] Vladimir Vapnik,et al. An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.
[13] David E. Rumelhart,et al. Predicting the Future: a Connectionist Approach , 1990, Int. J. Neural Syst..
[14] William A. Barnett,et al. Dynamic Econometric Modeling , 1988 .
[15] Dimitri P. Bertsekas,et al. Nonlinear Programming , 1997 .
[16] Claas de Groot,et al. Analysis of univariate time series with connectionist nets: A case study of two classical examples , 1991, Neurocomputing.
[17] Alexander J. Smola,et al. Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.
[18] Robert Goodell Brown,et al. Smoothing, forecasting and prediction of discrete time series , 1964 .
[19] Klaus-Robert Müller,et al. Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics , 1996, Neural Computation.
[20] Francis Eng Hock Tay,et al. A comparative study of saliency analysis and genetic algorithm for feature selection in support vector machines , 2001, Intell. Data Anal..
[21] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[22] Masanao Aoki,et al. State Space Modeling of Time Series , 1987 .
[23] J. Dziechciarz,et al. Changing and Random Coefficient Models: A Survey , 1989 .
[24] Lutgarde M. C. Buydens,et al. Using support vector machines for time series prediction , 2003 .
[25] Francis Eng Hock Tay,et al. Financial Forecasting Using Support Vector Machines , 2001, Neural Computing & Applications.
[26] Geoffrey E. Hinton,et al. Simplifying Neural Networks by Soft Weight-Sharing , 1992, Neural Computation.
[27] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[28] Gunnar Rätsch,et al. Predicting Time Series with Support Vector Machines , 1997, ICANN.
[29] F. Girosi,et al. Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.
[30] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[31] F. Tay,et al. Application of support vector machines in financial time series forecasting , 2001 .
[32] Gustavo Deco,et al. Non-parametric Data Selection for Neural Learning in Non-stationary Time Series , 1997, Neural Networks.
[33] Junhong Nie. Nonlinear time-series forecasting: A fuzzy-neural approach , 1997, Neurocomputing.
[34] Francis Eng Hock Tay,et al. Modified support vector machines in financial time series forecasting , 2002, Neurocomputing.
[35] Joydeep Ghosh,et al. Structurally adaptive modular networks for nonstationary environments , 1999, IEEE Trans. Neural Networks.
[36] Ruy Luiz Milidiú,et al. Time-series forecasting through wavelets transformation and a mixture of expert models , 1999, Neurocomputing.
[37] Krzysztof J. Cios,et al. Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model , 1996, Neurocomputing.
[38] A. Harvey. Time series models , 1983 .
[39] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[40] Derek W. Bunn,et al. Financial time series modelling with discounted least squares backpropagation , 1997, Neurocomputing.
[41] Klaus-Robert Müller,et al. Hidden Markov gating for prediction of change points in switching dynamical systems , 1999, ESANN.
[42] Zoran Obradovic,et al. Regime signaling techniques for non-stationary time series forecasting , 1997, Proceedings of the Thirtieth Hawaii International Conference on System Sciences.
[43] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[44] Ian T. Nabney,et al. Modelling financial time series with switching state space models , 1999, Proceedings of the IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering (CIFEr) (IEEE Cat. No.99TH8408).
[45] H. Tong,et al. Threshold Autoregression, Limit Cycles and Cyclical Data , 1980 .
[46] Gunnar Rätsch,et al. Using support vector machines for time series prediction , 1999 .