Detecting dynamical changes in time series by using the Jensen Shannon divergence

Most of the time series in nature are a mixture of signals with deterministic and random dynamics. Thus the distinction between these two characteristics becomes important. Distinguishing between chaotic and aleatory signals is difficult because they have a common wide band power spectrum, a delta l...

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Bibliographic Details
Published in:Chaos (Woodbury, N.Y.) N.Y.), 2017-08, Vol.27 (8), p.083118-083118
Main Authors: Mateos, D. M., Riveaud, L. E., Lamberti, P. W.
Format: Article
Language:English
Subjects:
Autocorrelation functions
Change detection
Chaos theory
Correlation analysis
Divergence
Information theory
Time series
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Summary:Most of the time series in nature are a mixture of signals with deterministic and random dynamics. Thus the distinction between these two characteristics becomes important. Distinguishing between chaotic and aleatory signals is difficult because they have a common wide band power spectrum, a delta like autocorrelation function, and share other features as well. In general, signals are presented as continuous records and require to be discretized for being analyzed. In this work, we introduce different schemes for discretizing and for detecting dynamical changes in time series. One of the main motivations is to detect transitions between the chaotic and random regime. The tools here used here originate from the Information Theory. The schemes proposed are applied to simulated and real life signals, showing in all cases a high proficiency for detecting changes in the dynamics of the associated time series.
ISSN:1054-1500
1089-7682
DOI:10.1063/1.4999613