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Predictability improvement as an asymmetrical measure of interdependence in bivariate time series

Feldmann, Ute and Bhattacharya, Joydeep. 2004. Predictability improvement as an asymmetrical measure of interdependence in bivariate time series. International Journal of Bifurcation and Chaos [In Applied Sciences and Engineering], 14(2), pp. 505-514. ISSN 02181274 [Article]

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Abstract or Description

In many signal processing applications, especially in the analysis of complex physiological systems, an important problem is to detect and quantify the interdependencies between signals (or time series). In this paper, we focus on asymmetrical relations between two time series with the aim of quantification of the directional influences between them in the sense of "who drives whom and how strongly". To meet this aim, we modify the mixed state analysis, which was proposed by Wiesenfeldt et al. [2001] to detect primarily the nature of the coupling (unidirectional or bidirectional), for the quantification of the strength of coupling in each direction. We introduce the predictability improvement of one time series by additional consideration of another time series. The newly developed measure is an analogue of the information theoretic concept of transfer entropy and is applicable to short time series. We demonstrate the application of this approach to coupled deterministic systems and to EEG data.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1142/S0218127404009314

Departments, Centres and Research Units:

Psychology

Dates:

DateEvent
2004Published

Item ID:

4960

Date Deposited:

21 Feb 2011 14:30

Last Modified:

30 Jun 2017 13:22

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/4960

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