标题： 符号高阶多变量时间序列分析 显示英文标题

标题： Symbolic Higher-Order Analysis of Multivariate Time Series

摘要： 从复杂系统各单元活动的时间测量中识别关系模式是一项具有许多实际应用的基础问题。 在这里，我们介绍了一种检测多元时间序列数据中任意阶依赖的方法。 该方法首先将多元时间序列转换为符号序列，然后通过贝叶斯方法提取统计上显著的符号字符串。 最后，这些主题模式被建模为超图的超边，使我们能够利用网络理论研究原始数据中的高阶交互。 当应用于神经和社交系统时，我们的方法揭示了有意义的高阶依赖性，突显了它们在大脑功能和社交行为中的重要性。 显示英文摘要

摘要： 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.