A Study on the Bayesian Recurrent Neural Network for Time Series Prediction |
Hong Chan-Young
(삼성전자)
Park Jung-Hoon (연세대학교 전기전자공학과) Yoon Tae-Sung (창원대학교 전기공학과) Park Jin-Bae (연세대학교 전기전자공학과) |
1 | 김해경, 김태수, 시계열 분석과 예측 이론, 경문사, 2003 |
2 | 이덕기, 예측 방법의 이해, 고려정보산업, 1999 |
3 | C. Chatfield, Time-series forecasting, Chapman & Hall/ CRC, 2000 |
4 | P. J. Brockwell and R. A. Davis, Introduction to time series and forecasting, Springer, New York, 2002 |
5 | A. Vehtari and J. Lampinen, 'Bayesian neural networks for industrial applications', Proceedings of the 1999 IEEE Midnight-Sun Workshop on Soft Computing Methods in Industrial Applications, pp. 63-68, 1999 DOI |
6 | M. Crucianu, R. Bone and J.-P. A. De Beauville, 'Bayesian learning for time series prediction with exogenous variables', International Joint Conference on Neural Networks, vol. 4, pp. 2594-2599, 1999 DOI |
7 | D. J. C. Mackay, 'A practical bayesian framework for backpropagation networks', Neural Computation, vol. 4, no. 3, pp. 448-472, 1992 DOI |
8 | J. F. D. Freitas, M. Niranjan and A. H. Gee, 'Hybrid sequential Monte carlo/kalman methods to train neural networks in non-stationary environments', IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 1057-1060, 1999 DOI |
9 | T. Zhang and A. Fukushige, 'Forecasting time series by bayesian neural networks', Proceedings of the 2002 International Joint Conference on Neural Networks, vol. 1, pp. 382-387, 2002 DOI |
10 | E. H. Tito, G. Zaverucha, M. Vellasco and M. Pacheco, 'Bayesian neural networks for electric load forecasting', 6th International Conference on Neural Information Processing, vol. 1, pp. 407 - 411, 1999 DOI |
11 | R. M. Neal, Bayesian learning for neural network, Lecture Notes in Statistics No. 118, Springer-Verlag, New York, 1996 |
12 | N. Bergman, Recursive Bayesian estimation : navigation and tracking applications, Linkoping University, 1999 |
13 | V. Kadirkamanathan and M. Niranjan, 'A function estimation approach to sequential learning with neural networks', Neural Computation, vol. 5, pp. 954-975, 1993 DOI ScienceOn |
14 | J. Durbin and S. J. Koopman, Time Series Analysis by State Space Methods, Oxford University Press, 2001 |
15 | N. J. Gordon, D. J. Salmond and A. F. M. Smith, 'Novel approach to nonlinear/non-Gaussian bayesian state estimation', Radar and Signal Processing, vol. 140, Issue 2, pp. 107-113, 1993 DOI |
16 | M. Crucianu, 'Bayesian learning for recurrent neural networks', Neurocomputing, 36, pp. 235-242, 2001 DOI ScienceOn |
17 | T. L. Song, 'Filtering theory', Journal of Control, Automation, and Systems Engineering, vol. 9, no. 6, pp. 413-419, 2003 DOI ScienceOn |
18 | D. Pena, G. C. Tiao and R. S. Tsay, A course in time series analysis, John Wiley & Sons, 2001 |
19 | J. Zhang, K. S. Tang and K. F. Man, 'Recurrent NN model for chaotic time series prediction', 23rd International Conference on Industrial Electronics, Control and Instrumentation, vol. 3, pp. 1108-1112, 1997 DOI |
20 | C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995 |
21 | G. C. Franco and R. C. Souza, 'A comparison of methods for bootstrapping in the local level model', Journal of Forecasting, 21, pp. 27-38, 2002 DOI ScienceOn |
22 | G. Kitagawa, 'Non-gaussian state-space modeling of nonstationary time series', Journal of the American Statistical Association, vol. 82, no. 400, pp. 1032-1036, 1987 DOI |
23 | O. A. Alsayegh, 'Annual energy consumption prediction using particle filters', Seventh International Symposium on Signal Processing and Its Applications, vol. 2, pp. 571-574, 2003 DOI |
24 | A. C. Tsakoumis, S. S. Vladov and V. M. Mladenov, 'Electric load forecasting with multilayer perceptron and elman neural network', 2002 6th Seminar on Neural Network Applications in Electrical Engineering, pp. 87-90, 2002 |
25 | R. G. Brown and P. Y. C. Hwang, Introduction to random signals and applied Kalman filter, John Wiley & Sons, 1997 |