 Detecting multifractality and exposing distributions of local fluctuations: Detrended fluctuation analysis with descriptive statistics poolingŞahin Telli, Hongzhuan Chen and Xufeng Zhao Chaos, Solitons & Fractals, 2022, vol. 155, issue C Abstract: In this article, DFA is expanded to detect multifractality in time series and to reveal the distributions of fluctuations. The method, Detrended Fluctuation Analysis with Descriptive Statistics Pooling (DFA-DSP), is introduced, tested and studied. Following the introduction of the method, robustness tests are conducted on computer generated data sets and compared with MF-DFA results. The MF-DFA findings for q=2 are identical to the DFA-DSP findings for a certain parameter value. Tests showed that the proposed method can be used to detect and measure multifractality in the time series. Given real data, similar results to those reported in the literature using MF-DFA were obtained. Furthermore, using min and max as pooling functions, the suggested method imitates the MF-DFA results for small and large fluctuations. Keywords: Hurst exponent; Multifractality; DFA; MF-DFA; Local fluctuations; Pooling function; Bitcoin; S&P 500 (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:155:y:2022:i:c:s0960077921010328 DOI: 10.1016/j.chaos.2021.111678 Access Statistics for this article Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros More articles in Chaos, Solitons & Fractals from Elsevier |
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