Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series
Abstract Distinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science. Compression-Complexity Causality (CCC) is a recently proposed interventional measure of causality, inspired by Wiener–Granger’s idea. It esti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-18288-4 |
| _version_ | 1798038786538471424 |
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| author | Aditi Kathpalia Pouya Manshour Milan Paluš |
| author_facet | Aditi Kathpalia Pouya Manshour Milan Paluš |
| author_sort | Aditi Kathpalia |
| collection | DOAJ |
| description | Abstract Distinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science. Compression-Complexity Causality (CCC) is a recently proposed interventional measure of causality, inspired by Wiener–Granger’s idea. It estimates causality based on change in dynamical compression-complexity (or compressibility) of the effect variable, given the cause variable. CCC works with minimal assumptions on given data and is robust to irregular-sampling, missing-data and finite-length effects. However, it only works for one-dimensional time series. We propose an ordinal pattern symbolization scheme to encode multidimensional patterns into one-dimensional symbolic sequences, and thus introduce the Permutation CCC (PCCC). We demonstrate that PCCC retains all advantages of the original CCC and can be applied to data from multidimensional systems with potentially unobserved variables which can be reconstructed using the embedding theorem. PCCC is tested on numerical simulations and applied to paleoclimate data characterized by irregular and uncertain sampling and limited numbers of samples. |
| first_indexed | 2024-04-11T21:45:07Z |
| format | Article |
| id | doaj.art-a420813810034798bd05f5f3736adfd6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T21:45:07Z |
| publishDate | 2022-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-a420813810034798bd05f5f3736adfd62022-12-22T04:01:26ZengNature PortfolioScientific Reports2045-23222022-08-0112111410.1038/s41598-022-18288-4Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time seriesAditi Kathpalia0Pouya Manshour1Milan Paluš2Department of Complex Systems, Institute of Computer Science of the Czech Academy of SciencesDepartment of Complex Systems, Institute of Computer Science of the Czech Academy of SciencesDepartment of Complex Systems, Institute of Computer Science of the Czech Academy of SciencesAbstract Distinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science. Compression-Complexity Causality (CCC) is a recently proposed interventional measure of causality, inspired by Wiener–Granger’s idea. It estimates causality based on change in dynamical compression-complexity (or compressibility) of the effect variable, given the cause variable. CCC works with minimal assumptions on given data and is robust to irregular-sampling, missing-data and finite-length effects. However, it only works for one-dimensional time series. We propose an ordinal pattern symbolization scheme to encode multidimensional patterns into one-dimensional symbolic sequences, and thus introduce the Permutation CCC (PCCC). We demonstrate that PCCC retains all advantages of the original CCC and can be applied to data from multidimensional systems with potentially unobserved variables which can be reconstructed using the embedding theorem. PCCC is tested on numerical simulations and applied to paleoclimate data characterized by irregular and uncertain sampling and limited numbers of samples.https://doi.org/10.1038/s41598-022-18288-4 |
| spellingShingle | Aditi Kathpalia Pouya Manshour Milan Paluš Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series Scientific Reports |
| title | Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series |
| title_full | Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series |
| title_fullStr | Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series |
| title_full_unstemmed | Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series |
| title_short | Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series |
| title_sort | compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series |
| url | https://doi.org/10.1038/s41598-022-18288-4 |
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