Concurrence: A dependence criterion for time series, applied to biological data

TL;DR

The paper introduces 'concurrence,' a new dependence criterion for time series that uses classifier-based distinction between aligned and misaligned segments, applicable across various biological signals.

Contribution

It presents a novel classifier-based dependence criterion, 'concurrence,' capable of detecting complex relationships in biological time series without extensive parameter tuning.

Findings

Concurrence effectively detects dependencies in biological signals.

The criterion is theoretically linked to dependence concepts.

Applicable to diverse data types like fMRI and physiological signals.

Abstract

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.