Coupling detrended fluctuation analysis for multiple warehouse-out behavioral sequences

Can-Zhong Yao, Ji-Nan Lin and Xu-Zhou Zheng

Physica A: Statistical Mechanics and its Applications, 2017, vol. 465, issue C, 75-90

Abstract: Interaction patterns among different warehouses could make the warehouse-out behavioral sequences less predictable. We firstly take a coupling detrended fluctuation analysis on the warehouse-out quantity, and find that the multivariate sequences exhibit significant coupling multifractal characteristics regardless of the types of steel products. Secondly, we track the sources of multifractal warehouse-out sequences by shuffling and surrogating original ones, and we find that fat-tail distribution contributes more to multifractal features than the long-term memory, regardless of types of steel products. From perspective of warehouse contribution, some warehouses steadily contribute more to multifractal than other warehouses. Finally, based on multiscale multifractal analysis, we propose Hurst surface structure to investigate coupling multifractal, and show that multiple behavioral sequences exhibit significant coupling multifractal features that emerge and usually be restricted within relatively greater time scale interval.

Keywords: CDFA; Logistics system; Multifractal; MMA; Fat-tail distribution; Non-linear coupling relationship (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:465:y:2017:i:c:p:75-90

DOI: 10.1016/j.physa.2016.08.016

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