Using missing ordinal patterns to detect nonlinearity in time series data
The number of missing ordinal patterns (NMP) is the number of ordinal patterns that do not appear in a series after it has been symbolized using the Bandt and Pompe methodology. In this paper, the NMP is demonstrated as a test for nonlinearity using a surrogate framework in order to see if the NMP f...
| Authors: | , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2017 |
| Country: | Argentina |
| Institution: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repository: | CONICET Digital (CONICET) |
| Language: | English |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/49242 |
| Online Access: | http://hdl.handle.net/11336/49242 |
| Access Level: | Open access |
| Keyword: | Time Series Analysis Nonlinearity Missing Ordinal Patterns Surrogate Method https://purl.org/becyt/ford/1.3 https://purl.org/becyt/ford/1 |
| Summary: | The number of missing ordinal patterns (NMP) is the number of ordinal patterns that do not appear in a series after it has been symbolized using the Bandt and Pompe methodology. In this paper, the NMP is demonstrated as a test for nonlinearity using a surrogate framework in order to see if the NMP for a series is statistically different from the NMP of iterative amplitude adjusted Fourier transform (IAAFT) surrogates. It is found that the NMP works well as a test statistic for nonlinearity, even in the cases of very short time series. Both model and experimental time series are used to demonstrate the efficacy of the NMP as a test for nonlinearity. |
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