Evaluating Methods for Detrending Time Series Using Ordinal Patterns, with an Application to Air Transport Delays
Functional networks have become a standard tool for the analysis of complex systems, allowing the unveiling of their internal connectivity structure while only requiring the observation of the system’s constituent dynamics. To obtain reliable results, one (often overlooked) prerequisite involves the...
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| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/3/230 |
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| author | Felipe Olivares F. Javier Marín-Rodríguez Kishor Acharya Massimiliano Zanin |
| author_facet | Felipe Olivares F. Javier Marín-Rodríguez Kishor Acharya Massimiliano Zanin |
| author_sort | Felipe Olivares |
| collection | DOAJ |
| description | Functional networks have become a standard tool for the analysis of complex systems, allowing the unveiling of their internal connectivity structure while only requiring the observation of the system’s constituent dynamics. To obtain reliable results, one (often overlooked) prerequisite involves the stationarity of an analyzed time series, without which spurious functional connections may emerge. Here, we show how ordinal patterns and metrics derived from them can be used to assess the effectiveness of detrending methods. We apply this approach to data representing the evolution of delays in major European and US airports, and to synthetic versions of the same, obtaining operational conclusions about how these propagate in the two systems. |
| format | Article |
| id | doaj-art-076a4d4fe05c44b2869c1a73ddd8e3df |
| institution | DOAJ |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-076a4d4fe05c44b2869c1a73ddd8e3df2025-08-20T02:42:29ZengMDPI AGEntropy1099-43002025-02-0127323010.3390/e27030230Evaluating Methods for Detrending Time Series Using Ordinal Patterns, with an Application to Air Transport DelaysFelipe Olivares0F. Javier Marín-Rodríguez1Kishor Acharya2Massimiliano Zanin3Instituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus UIB, 07122 Palma, SpainInstituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus UIB, 07122 Palma, SpainInstituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus UIB, 07122 Palma, SpainInstituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus UIB, 07122 Palma, SpainFunctional networks have become a standard tool for the analysis of complex systems, allowing the unveiling of their internal connectivity structure while only requiring the observation of the system’s constituent dynamics. To obtain reliable results, one (often overlooked) prerequisite involves the stationarity of an analyzed time series, without which spurious functional connections may emerge. Here, we show how ordinal patterns and metrics derived from them can be used to assess the effectiveness of detrending methods. We apply this approach to data representing the evolution of delays in major European and US airports, and to synthetic versions of the same, obtaining operational conclusions about how these propagate in the two systems.https://www.mdpi.com/1099-4300/27/3/230time seriesstationarityfunctional complex networksordinal patternscausality |
| spellingShingle | Felipe Olivares F. Javier Marín-Rodríguez Kishor Acharya Massimiliano Zanin Evaluating Methods for Detrending Time Series Using Ordinal Patterns, with an Application to Air Transport Delays Entropy time series stationarity functional complex networks ordinal patterns causality |
| title | Evaluating Methods for Detrending Time Series Using Ordinal Patterns, with an Application to Air Transport Delays |
| title_full | Evaluating Methods for Detrending Time Series Using Ordinal Patterns, with an Application to Air Transport Delays |
| title_fullStr | Evaluating Methods for Detrending Time Series Using Ordinal Patterns, with an Application to Air Transport Delays |
| title_full_unstemmed | Evaluating Methods for Detrending Time Series Using Ordinal Patterns, with an Application to Air Transport Delays |
| title_short | Evaluating Methods for Detrending Time Series Using Ordinal Patterns, with an Application to Air Transport Delays |
| title_sort | evaluating methods for detrending time series using ordinal patterns with an application to air transport delays |
| topic | time series stationarity functional complex networks ordinal patterns causality |
| url | https://www.mdpi.com/1099-4300/27/3/230 |
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