Efficiently Measuring Complexity on the Basis of Real-World Data
Permutation entropy, introduced by Bandt and Pompe, is a conceptually simple and well-interpretable measure of time series complexity. In this paper, we propose efficient methods for computing it and related ordinal-patterns-based characteristics. The methods are based on precomputing values of succ...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-10-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/15/10/4392 |
| _version_ | 1811187362989867008 |
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| author | Valentina A. Unakafova Karsten Keller |
| author_facet | Valentina A. Unakafova Karsten Keller |
| author_sort | Valentina A. Unakafova |
| collection | DOAJ |
| description | Permutation entropy, introduced by Bandt and Pompe, is a conceptually simple and well-interpretable measure of time series complexity. In this paper, we propose efficient methods for computing it and related ordinal-patterns-based characteristics. The methods are based on precomputing values of successive ordinal patterns of order d, considering the fact that they are “overlapped” in d points, and on precomputing successive values of the permutation entropy related to “overlapping” successive time-windows. The proposed methods allow for measurement of the complexity of very large datasets in real-time. |
| first_indexed | 2024-04-11T14:02:03Z |
| format | Article |
| id | doaj.art-f15011c2e3c74b07adfc0e026ae6c074 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T14:02:03Z |
| publishDate | 2013-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-f15011c2e3c74b07adfc0e026ae6c0742022-12-22T04:20:06ZengMDPI AGEntropy1099-43002013-10-0115104392441510.3390/e15104392Efficiently Measuring Complexity on the Basis of Real-World DataValentina A. UnakafovaKarsten KellerPermutation entropy, introduced by Bandt and Pompe, is a conceptually simple and well-interpretable measure of time series complexity. In this paper, we propose efficient methods for computing it and related ordinal-patterns-based characteristics. The methods are based on precomputing values of successive ordinal patterns of order d, considering the fact that they are “overlapped” in d points, and on precomputing successive values of the permutation entropy related to “overlapping” successive time-windows. The proposed methods allow for measurement of the complexity of very large datasets in real-time.http://www.mdpi.com/1099-4300/15/10/4392permutation entropyordinal patternsefficient computingcomplexity |
| spellingShingle | Valentina A. Unakafova Karsten Keller Efficiently Measuring Complexity on the Basis of Real-World Data Entropy permutation entropy ordinal patterns efficient computing complexity |
| title | Efficiently Measuring Complexity on the Basis of Real-World Data |
| title_full | Efficiently Measuring Complexity on the Basis of Real-World Data |
| title_fullStr | Efficiently Measuring Complexity on the Basis of Real-World Data |
| title_full_unstemmed | Efficiently Measuring Complexity on the Basis of Real-World Data |
| title_short | Efficiently Measuring Complexity on the Basis of Real-World Data |
| title_sort | efficiently measuring complexity on the basis of real world data |
| topic | permutation entropy ordinal patterns efficient computing complexity |
| url | http://www.mdpi.com/1099-4300/15/10/4392 |
| work_keys_str_mv | AT valentinaaunakafova efficientlymeasuringcomplexityonthebasisofrealworlddata AT karstenkeller efficientlymeasuringcomplexityonthebasisofrealworlddata |