Adaptive Time Series Analysis Using Predictive Inference and Entropy
Raman K. Mehra, Shah Mahmood · STIN · 1990
Abstract : Research is reported on adaptive time series methods for detecting and tracking both abrupt and slow changes in both structure and parameters of dynamic systems. The methods are based on a unified statistical framework which is motivated by statistical inferences and entropy arguments. The method yields estimates of multivariate input/output dynamics and noise statistics. It also gives estimate of system order that is optimal in the sense of an information theoretic criterion. The integrated approach is known as CVA-AIC. Many theoretical issues have been explored under the scope of this project. The relationship between this technique and another powerful framework for estimation known as E-M algorithmic approach has been established. It the CVA- AIC technique is embedded properly in an E-M framework, it leads to maximum likelihood estimates and recursive algorithms for system identification. (jhd)