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Detrending fluctuation analysis based on moving average filtering

✍ Scribed by Jose Alvarez-Ramirez; Eduardo Rodriguez; Juan Carlos Echeverría

Publisher
Elsevier Science
Year
2005
Tongue
English
Weight
548 KB
Volume
354
Category
Article
ISSN
0378-4371

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✦ Synopsis

Detrended fluctuation analysis (DFA) is a scaling method commonly used for detecting long-range correlations in nonstationary time series. Applications range from financial time series to physiological data. However, as the removal of trends in DFA is based on discontinuous polynomial fitting, oscillations in the fluctuation function and significant errors in crossover locations can be introduced. To reduce the problems induced by discontinuous fitting, moving average (MA) methods have been proposed previously by Alesio et al. (Eur. J. Phys. B 27 (2002) 197). In this work, a variant of such MA methods is studied; specifically, the performance and characteristics of a MA method based on central differences is studied. Some important properties of this MA method are analyzed and illustrated with several artificial and real time series.

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