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Decomposing the effect of anomalous diffusion enables direct calculation of the Hurst exponent and model classification for single random paths
- Recently, a large number of research teams from around the world collaborated in the so-called 'anomalous diffusion challenge'. Its aim: to develop and compare new techniques for inferring stochastic models from given unknown time series, and estimate the anomalous diffusion exponent in data. We use various numerical methods to directly obtain this exponent using the path increments, and develop a questionnaire for model selection based on feature analysis of a set of known stochastic processes given as candidates. Here, we present the theoretical background of the automated algorithm which we put for these tasks in the diffusion challenge, as a counter to other pure data-driven approaches.
Author details: | Philipp MeyerORCiD, Erez AghionORCiD, Holger KantzORCiDGND |
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DOI: | https://doi.org/10.1088/1751-8121/ac72d4 |
ISSN: | 1751-8113 |
ISSN: | 1751-8121 |
Title of parent work (English): | Journal of physics / Institute of Physics. A, Mathematical, nuclear and general |
Publisher: | IOP Publ. Ltd. |
Place of publishing: | Bristol |
Publication type: | Article |
Language: | English |
Date of first publication: | 2022/06/13 |
Publication year: | 2022 |
Release date: | 2024/03/01 |
Tag: | anomalous; decomposing anomalous diffusion; diffusion exponent; process inference; time-series analysis |
Volume: | 55 |
Issue: | 27 |
Article number: | 274001 |
Number of pages: | 22 |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie |
DDC classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
Peer review: | Referiert |