Title :
An On-Line Change Detection Scheme for Nonlinear Time Series Data from Unknown Dynamical Systems: A Bayesian Appraoch Using Sequential Monte Carlo
Author :
Nakada, Yohei ; Matsumoto, Takashi
Author_Institution :
Graduate Sch. of Sci. & Eng., Waseda Univ., Tokyo
Abstract :
This paper attempts to perform on-line change detection given time series data from unknown nonlinear dynamical systems. In the algorithm, the probability of occurrence of an abrupt change is estimated within a Bayesian framework. The implementation is done via sequential Monte Carlo (SMC). The proposed scheme is tested against two examples with nonlinear dynamical systems
Keywords :
Bayes methods; Monte Carlo methods; data analysis; nonlinear dynamical systems; time series; Bayesian approach; abrupt change occurrence probability; nonlinear dynamical systems; nonlinear time series data; online change detection; sequential Monte Carlo; unknown dynamical systems; Bayesian methods; Change detection algorithms; Data analysis; Monte Carlo methods; Nonlinear dynamical systems; Nonlinear systems; Signal processing algorithms; Sliding mode control; Testing; Uncertainty;
Conference_Titel :
Machine Learning for Signal Processing, 2005 IEEE Workshop on
Conference_Location :
Mystic, CT
Print_ISBN :
0-7803-9517-4
DOI :
10.1109/MLSP.2005.1532922
