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arXiv · 2506.00508

Symbolic Higher-Order Analysis of Multivariate Time Series

Andrea Civilini·Fabrizio de Vico Fallani·Vito Latora
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Abstract

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.

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Andrea Civilini, Fabrizio de Vico Fallani, Vito Latora. 2025-05-31. Symbolic Higher-Order Analysis of Multivariate Time Series. https://arxiv.org/abs/2506.00508

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