Quantifying long-range correlations with a multiscale ordinal pattern approach
In this paper we use the ordinal patterns probabilities associated with fractional Brownian motions for estimating the Hurst exponent of artificially generated and experimentally measured data. Numerical analysis show a reliable estimation of this scaling parameter, even when data with low resolutio...
| Authors: | , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2016 |
| Country: | Argentina |
| Institution: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repository: | CONICET Digital (CONICET) |
| Language: | English |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/43403 |
| Online Access: | http://hdl.handle.net/11336/43403 |
| Access Level: | Open access |
| Keyword: | Ordinal Patterns Probabilities Fractional Brownian Motion Hurst Exponent Multiscale Analysis https://purl.org/becyt/ford/1.3 https://purl.org/becyt/ford/1 |
| Summary: | In this paper we use the ordinal patterns probabilities associated with fractional Brownian motions for estimating the Hurst exponent of artificially generated and experimentally measured data. Numerical analysis show a reliable estimation of this scaling parameter, even when data with low resolution are analysed. Robustness to observational noise is also obtained. Several experimental applications allow us to confirm the practical utility of the proposed approach. We contrast results obtained by implementing this multiscale symbolic tool with those obtained from the classical detrended fluctuation analysis. |
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