通过时序对齐与错位片段分类判断信号依赖性，无需大量数据或先验知识。
Concurrence: A dependence criterion for time series, applied to biological data
- 用分类器区分对齐与错位片段来判断时间序列是否相关。
- 在fMRI、生理和行为数据上均有效，适用范围广。
- 无需参数调优或大数据，适合跨学科科学分析。
衡量观测信号间的统计依赖性是科学发现的重要工具。然而，生物系统常表现出复杂的非线性相互作用，现有方法通常需要先验知识或大量数据才能捕捉。本文提出一种依赖性判定准则——共现性（concurrence）：若能通过分类器区分从两个时间序列中提取的时序对齐与错位片段，则认为二者存在依赖关系。我们证明该准则在理论上与依赖性相关，并可作为跨学科科学分析的标准方法，在不依赖特定参数调优或海量数据的前提下，揭示多种信号（如fMRI、生理及行为数据）间的广泛关联。
原文摘要 · Abstract (English)
Measuring the statistical dependence between observed signals is a primary tool for scientific discovery. However, biological systems often exhibit complex non-linear interactions that currently cannot be captured without a priori knowledge or large datasets. We introduce a criterion for dependence, whereby two time series are deemed dependent if one can construct a classifier that distinguishes between temporally aligned vs. misaligned segments extracted from them. We show that this criterion, concurrence, is theoretically linked with dependence, and can become a standard approach for scientific analyses across disciplines, as it can expose relationships across a wide spectrum of signals (fMRI, physiological and behavioral data) without ad-hoc parameter tuning or large amounts of data.
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