Symbolic Higher-Order Analysis of Multivariate Time Series
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912946657951744 |
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| author | Civilini, Andrea Fallani, Fabrizio de Vico Latora, Vito |
| author_facet | Civilini, Andrea Fallani, Fabrizio de Vico Latora, Vito |
| contents | Identifying patterns of relations among the units of a complex system from measurements of their activities in time is a fundamental problem with many practical applications. Here, we introduce a method that detects dependencies of any order in multivariate time series data. The method first transforms a multivariate time series into a symbolic sequence, and then extract statistically significant strings of symbols through a Bayesian approach. Such motifs are finally modelled as the hyperedges of a hypergraph, allowing us to use network theory to study higher-order interactions in the original data. When applied to neural and social systems, our method reveals meaningful higher-order dependencies, highlighting their importance in both brain function and social behaviour. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00508 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Symbolic Higher-Order Analysis of Multivariate Time Series Civilini, Andrea Fallani, Fabrizio de Vico Latora, Vito Physics and Society Social and Information Networks Applied Physics Data Analysis, Statistics and Probability Identifying patterns of relations among the units of a complex system from measurements of their activities in time is a fundamental problem with many practical applications. Here, we introduce a method that detects dependencies of any order in multivariate time series data. The method first transforms a multivariate time series into a symbolic sequence, and then extract statistically significant strings of symbols through a Bayesian approach. Such motifs are finally modelled as the hyperedges of a hypergraph, allowing us to use network theory to study higher-order interactions in the original data. When applied to neural and social systems, our method reveals meaningful higher-order dependencies, highlighting their importance in both brain function and social behaviour. |
| title | Symbolic Higher-Order Analysis of Multivariate Time Series |
| topic | Physics and Society Social and Information Networks Applied Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2506.00508 |