A method for segmentation of switching dynamic modes in time series

Authors:
Lei Feng;Kihwan Ju;K. H. Chon
Affiliations:
Dept. of Biomed. Eng., State Univ. of New York, Stony Brook, NY, USA;-;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2005

Citing 0
Cited 4

RLDDE: A novel reinforcement learning-based dimension and delay estimator for neural networks in time series prediction

Neurocomputing
Representing financial time series based on data point importance

Engineering Applications of Artificial Intelligence
Cluster-based genetic segmentation of time series with DWT

Pattern Recognition Letters
A review on time series data mining

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

A method to identify switching dynamics in time series, based on Annealed Competition of Experts algorithm (ACE), has been developed by Kohlmorgen et al. Incorrect selection of embedding dimension and time delay of the signal significantly affect the performance of the ACE method, however. In this paper, we utilize systematic approaches based on mutual information and false nearest neighbor to determine appropriate embedding dimension and time delay. Moreover, we obtained further improvements to the original ACE method by incorporating a deterministic annealing approach as well as phase space closeness measure. Using these improved implementations, we have enhanced the performance of the ACE algorithm in determining the location of the switching of dynamic modes in the time series. The application of the improved ACE method to heart rate data obtained from rats during control and administration of double autonomic blockade conditions indicate that the improved ACE algorithm is able to segment dynamic mode changes with pinpoint accuracy and that its performance is superior to the original ACE algorithm.