Assessing the Preprocessing Benefits of Data-Driven Decomposition Methods for Phase Permutation Entropy—Application to Econometric Time Series
This paper investigates the efficacy of various data-driven decomposition methods combined with Phase Permutation Entropy (PPE) to form a promising complexity metric for analyzing time series. PPE is a variant of classical permutation entropy (PE), while the examined data-driven decomposition method...
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MDPI AG
2024-07-01
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| author | Erwan Pierron Meryem Jabloun |
| author_facet | Erwan Pierron Meryem Jabloun |
| author_sort | Erwan Pierron |
| collection | DOAJ |
| description | This paper investigates the efficacy of various data-driven decomposition methods combined with Phase Permutation Entropy (PPE) to form a promising complexity metric for analyzing time series. PPE is a variant of classical permutation entropy (PE), while the examined data-driven decomposition methods include Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Empirical Wavelet Transform (EWT), Seasonal and Trend decomposition using Loess (STL), and Singular Spectrum Analysis-based decomposition (SSA). To our knowledge, this combination has not been explored yet. Our primary aim is to assess how these preprocessing methods affect PPE’s ability to capture temporal structural complexities within time series. This evaluation encompasses the analysis of both simulated and econometric time series. Our results reveal that combining SSA with PPE produces superior advantages for measuring the complexity of seasonal time series. Conversely, VMD combined with PPE proves to be the less advantageous strategy. Overall, our study illustrates that combining data-driven preprocessing methods with PPE offers greater benefits compared to combining them with traditional PE in quantifying time series complexity. |
| format | Article |
| id | doaj-art-3e5570f53edf44abbcead4223ea7accd |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-3e5570f53edf44abbcead4223ea7accd2025-08-20T02:11:25ZengMDPI AGEngineering Proceedings2673-45912024-07-016812810.3390/engproc2024068028Assessing the Preprocessing Benefits of Data-Driven Decomposition Methods for Phase Permutation Entropy—Application to Econometric Time SeriesErwan Pierron0Meryem Jabloun1Prisme Laboratory, Université d’Orléans, 12 Rue de Blois, 45067 Orléans, FrancePrisme Laboratory, Université d’Orléans, 12 Rue de Blois, 45067 Orléans, FranceThis paper investigates the efficacy of various data-driven decomposition methods combined with Phase Permutation Entropy (PPE) to form a promising complexity metric for analyzing time series. PPE is a variant of classical permutation entropy (PE), while the examined data-driven decomposition methods include Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Empirical Wavelet Transform (EWT), Seasonal and Trend decomposition using Loess (STL), and Singular Spectrum Analysis-based decomposition (SSA). To our knowledge, this combination has not been explored yet. Our primary aim is to assess how these preprocessing methods affect PPE’s ability to capture temporal structural complexities within time series. This evaluation encompasses the analysis of both simulated and econometric time series. Our results reveal that combining SSA with PPE produces superior advantages for measuring the complexity of seasonal time series. Conversely, VMD combined with PPE proves to be the less advantageous strategy. Overall, our study illustrates that combining data-driven preprocessing methods with PPE offers greater benefits compared to combining them with traditional PE in quantifying time series complexity.https://www.mdpi.com/2673-4591/68/1/28time seriespermutation entropyphase permutation entropydata-driven signal decompositionEMDVMD |
| spellingShingle | Erwan Pierron Meryem Jabloun Assessing the Preprocessing Benefits of Data-Driven Decomposition Methods for Phase Permutation Entropy—Application to Econometric Time Series Engineering Proceedings time series permutation entropy phase permutation entropy data-driven signal decomposition EMD VMD |
| title | Assessing the Preprocessing Benefits of Data-Driven Decomposition Methods for Phase Permutation Entropy—Application to Econometric Time Series |
| title_full | Assessing the Preprocessing Benefits of Data-Driven Decomposition Methods for Phase Permutation Entropy—Application to Econometric Time Series |
| title_fullStr | Assessing the Preprocessing Benefits of Data-Driven Decomposition Methods for Phase Permutation Entropy—Application to Econometric Time Series |
| title_full_unstemmed | Assessing the Preprocessing Benefits of Data-Driven Decomposition Methods for Phase Permutation Entropy—Application to Econometric Time Series |
| title_short | Assessing the Preprocessing Benefits of Data-Driven Decomposition Methods for Phase Permutation Entropy—Application to Econometric Time Series |
| title_sort | assessing the preprocessing benefits of data driven decomposition methods for phase permutation entropy application to econometric time series |
| topic | time series permutation entropy phase permutation entropy data-driven signal decomposition EMD VMD |
| url | https://www.mdpi.com/2673-4591/68/1/28 |
| work_keys_str_mv | AT erwanpierron assessingthepreprocessingbenefitsofdatadrivendecompositionmethodsforphasepermutationentropyapplicationtoeconometrictimeseries AT meryemjabloun assessingthepreprocessingbenefitsofdatadrivendecompositionmethodsforphasepermutationentropyapplicationtoeconometrictimeseries |