{"ID":2828253,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16001","arxiv_id":"2512.16001","title":"Concurrence: A dependence criterion for time series, applied to biological data","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.","short_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 se...","url_abs":"https://arxiv.org/abs/2512.16001","url_pdf":"https://arxiv.org/pdf/2512.16001v2","authors":"[\"Evangelos Sariyanidi\",\"John D. Herrington\",\"Lisa Yankowitz\",\"Pratik Chaudhari\",\"Theodore D. Satterthwaite\",\"Casey J. Zampella\",\"Jeffrey S. Morris\",\"Edward Gunning\",\"Robert T. Schultz\",\"Russell T. Shinohara\",\"Birkan Tunc\"]","published":"2025-12-17T22:10:39Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.LG\"]","methods":"[]","has_code":false}