Detection of crossover time scales in multifractal detrended fluctuation analysis
Publication in refereed journal


Times Cited
Web of Science22WOS source URL (as at 13/05/2021) Click here for the latest count
Altmetrics Information
.

Other information
AbstractFractal is employed in this paper as a scale-based method for the identification of the scaling behavior of time series. Many spatial and temporal processes exhibiting complex multi(mono)-scaling behaviors are fractals. One of the important concepts in fractals is crossover time scale(s) that separates distinct regimes having different fractal scaling behaviors. A common method is multifractal detrended fluctuation analysis (MF-DFA). The detection of crossover time scale(s) is, however, relatively subjective since it has been made without rigorous statistical procedures and has generally been determined by eye balling or subjective observation. Crossover time scales such determined may be spurious and problematic. It may not reflect the genuine underlying scaling behavior of a time series. The purpose of this paper is to propose a statistical procedure to model complex fractal scaling behaviors and reliably identify the crossover time scales under MF-DFA. The scaling-identification regression model, grounded on a solid statistical foundation, is first proposed to describe multi-scaling behaviors of fractals. Through the regression analysis and statistical inference, we can (1) identify the crossover time scales that cannot be detected by eye-balling observation, (2) determine the number and locations of the genuine crossover time scales, (3) give confidence intervals for the crossover time scales, and (4) establish the statistically significant regression model depicting the underlying scaling behavior of a time series. To substantive our argument, the regression model is applied to analyze the multi-scaling behaviors of avian-influenza outbreaks, water consumption, daily mean temperature, and rainfall of Hong Kong. Through the proposed model, we can have a deeper understanding of fractals in general and a statistical approach to identify multi-scaling behavior under MF-DFA in particular.
All Author(s) ListGe EJ, Leung Y
Journal nameJournal of Geographical Systems
Year2013
Month4
Day1
Volume Number15
Issue Number2
PublisherSPRINGER HEIDELBERG
Pages115 - 147
ISSN1435-5930
eISSN1435-5949
LanguagesEnglish-United Kingdom
KeywordsCrossover time scale; Multifractal detrended fluctuation analysis; Scaling behavior; Scaling-identification regression model; Time series
Web of Science Subject CategoriesGeography; GEOGRAPHY

Last updated on 2021-13-05 at 23:10