Title :
Bayesian MCMC nonlinear time series prediction
Author :
Nakada, Y. ; Kurihara, T. ; Matsumoto, T.
Author_Institution :
Dept. of Electr., Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan
Abstract :
A MCMC (Markov chain Monte Carlo) algorithm is proposed for nonlinear time series prediction with a hierarchical Bayesian framework. The algorithm computes predictive mean and error bar by drawing samples from predictive distributions. The algorithm is tested against time series generated by a (chaotic) Rossler system and it outperforms quadratic approximations previously proposed by the authors
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; prediction theory; signal sampling; time series; Bayesian prediction; MCMC algorithm; Markov chain Monte Carlo algorithm; chaotic Rossler system; error bar; hierarchical Bayesian framework; nonlinear time series; predictive mean; samples; Bayesian methods; Chaos; Density measurement; Fluid flow measurement; Markov processes; Monte Carlo methods; Neural networks; Prediction algorithms; Time measurement; Uncertainty;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Conference_Location :
Salt Lake City, UT
Print_ISBN :
0-7803-7041-4
DOI :
10.1109/ICASSP.2001.940598
