Coarse-Grained Entropy Rates for Characterization of Complex Time Series

Milan Palu\v S

Working Papers from Santa Fe Institute

Abstract: A method for estimation of coarse-grained entropy rates (CER's) from time series is presented, based on information-theoretic functionals---redundancies. The CER's are relative measures of regularity and predictability, and for data generated by dynamical systems they are related to Kolmogorov-Sinai entropy. A deterministic dymanical origin of the data under study, however, is not a condition necessary for the use of the CER's, since the entropy rates can be defined for stochastic processes as well. Sensitivity of the CER's to changes in the data dynamics is tested by numerically generated time series resulted from both deterministic---chaotic and stochastic processes. Potential application of the CER's in analysis of electrophysiological signals or other complex time series is demonstrated by an example from a pharmaco-EEG study.

Date: 1994-06
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