Concurrence: A dependence criterion for time series, applied to biological data
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arXiv
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| Format: | Preprint |
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2025
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| author | Sariyanidi, Evangelos Herrington, John D. Yankowitz, Lisa Chaudhari, Pratik Satterthwaite, Theodore D. Zampella, Casey J. Morris, Jeffrey S. Gunning, Edward Schultz, Robert T. Shinohara, Russell T. Tunc, Birkan |
| author_facet | Sariyanidi, Evangelos Herrington, John D. Yankowitz, Lisa Chaudhari, Pratik Satterthwaite, Theodore D. Zampella, Casey J. Morris, Jeffrey S. Gunning, Edward Schultz, Robert T. Shinohara, Russell T. Tunc, Birkan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16001 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Concurrence: A dependence criterion for time series, applied to biological data Sariyanidi, Evangelos Herrington, John D. Yankowitz, Lisa Chaudhari, Pratik Satterthwaite, Theodore D. Zampella, Casey J. Morris, Jeffrey S. Gunning, Edward Schultz, Robert T. Shinohara, Russell T. Tunc, Birkan Signal Processing Machine Learning 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. |
| title | Concurrence: A dependence criterion for time series, applied to biological data |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2512.16001 |