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 |