Title: Concurrence: A dependence criterion for time series, applied to biological data
Primary area: learning on time series and dynamical systems
Keywords: dependence, time series, scientific analysis, correlation

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.

TL;DR: Measuring dependence between time series without ad-hoc parameter tuning