Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series
Here, Zanin and Olivares review the permutation patterns-based metrics used to distinguish chaos from stochasticity in discrete time series. They analyse their performance and computational cost, and compare their applicability to real-world time series.
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-08-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-021-00696-z |
| _version_ | 1818842311963967488 |
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| author | Massimiliano Zanin Felipe Olivares |
| author_facet | Massimiliano Zanin Felipe Olivares |
| author_sort | Massimiliano Zanin |
| collection | DOAJ |
| description | Here, Zanin and Olivares review the permutation patterns-based metrics used to distinguish chaos from stochasticity in discrete time series. They analyse their performance and computational cost, and compare their applicability to real-world time series. |
| first_indexed | 2024-12-19T04:39:58Z |
| format | Article |
| id | doaj.art-0d10f3333557449fbd1dd92ccc4eb942 |
| institution | Directory Open Access Journal |
| issn | 2399-3650 |
| language | English |
| last_indexed | 2024-12-19T04:39:58Z |
| publishDate | 2021-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Physics |
| spelling | doaj.art-0d10f3333557449fbd1dd92ccc4eb9422022-12-21T20:35:37ZengNature PortfolioCommunications Physics2399-36502021-08-014111410.1038/s42005-021-00696-zOrdinal patterns-based methodologies for distinguishing chaos from noise in discrete time seriesMassimiliano Zanin0Felipe Olivares1Instituto de Física Interdisciplinar y Sistemas Complejos CSIC-UIB, Edifici Instituts Universitaris de RecercaInstituto de Física Interdisciplinar y Sistemas Complejos CSIC-UIB, Edifici Instituts Universitaris de RecercaHere, Zanin and Olivares review the permutation patterns-based metrics used to distinguish chaos from stochasticity in discrete time series. They analyse their performance and computational cost, and compare their applicability to real-world time series.https://doi.org/10.1038/s42005-021-00696-z |
| spellingShingle | Massimiliano Zanin Felipe Olivares Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series Communications Physics |
| title | Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series |
| title_full | Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series |
| title_fullStr | Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series |
| title_full_unstemmed | Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series |
| title_short | Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series |
| title_sort | ordinal patterns based methodologies for distinguishing chaos from noise in discrete time series |
| url | https://doi.org/10.1038/s42005-021-00696-z |
| work_keys_str_mv | AT massimilianozanin ordinalpatternsbasedmethodologiesfordistinguishingchaosfromnoiseindiscretetimeseries AT felipeolivares ordinalpatternsbasedmethodologiesfordistinguishingchaosfromnoiseindiscretetimeseries |