A feature-based information-theoretic approach for detecting interpretable, long-timescale pairwise interactions from time series
arxiv(2024)
摘要
Quantifying relationships between components of a complex system is critical
to understanding the rich network of interactions that characterize the
behavior of the system. Traditional methods for detecting pairwise dependence
of time series, such as Pearson correlation, Granger causality, and mutual
information, are computed directly in the space of measured time-series values.
But for systems in which interactions are mediated by statistical properties of
the time series (`time-series features') over longer timescales, this approach
can fail to capture the underlying dependence from limited and noisy
time-series data, and can be challenging to interpret. Addressing these issues,
here we introduce an information-theoretic method for detecting dependence
between time series mediated by time-series features that provides
interpretable insights into the nature of the interactions. Our method extracts
a candidate set of time-series features from sliding windows of the source time
series and assesses their role in mediating a relationship to values of the
target process. Across simulations of three different generative processes, we
demonstrate that our feature-based approach can outperform a traditional
inference approach based on raw time-series values, especially in challenging
scenarios characterized by short time-series lengths, high noise levels, and
long interaction timescales. Our work introduces a new tool for inferring and
interpreting feature-mediated interactions from time-series data, contributing
to the broader landscape of quantitative analysis in complex systems research,
with potential applications in various domains including but not limited to
neuroscience, finance, climate science, and engineering.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要