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.
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/0d10f3333557449fbd1dd92ccc4eb942 |
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| Sumario: | 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. |
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