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

• URL: http://arxiv.org/abs/2512.16001v1
• Date: Wed, 17 Dec 2025 22:10:39 GMT
• Title: Concurrence: A dependence criterion for time series, applied to biological data
• 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,
• Abstract summary: 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.<n>We show that this criterion, concurrence, is theoretically linked with dependence, and can become a standard approach for scientific analyses across disciplines.
• Score: 13.344221983316658
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Measuring Dependencies between Biological Signals with Self-supervision, and its Limitations [17.74984497708191]
  We introduce a self-supervised approach, concurrence, which is inspired by the observation that if two signals are dependent, then one should be able to distinguish between temporally aligned vs. misaligned segments.<n>Experiments with fMRI, physiological and behavioral signals show that concurrence is the first approach that can expose relationships across such a wide spectrum of signals.
  arXiv  Detail & Related papers  (2025-07-29T21:15:13Z)
• Flow based approach for Dynamic Temporal Causal models with non-Gaussian or Heteroscedastic Noises [37.02662517645979]
  We introduce FANTOM, a unified framework for causal discovery.<n>It handles non-stationary processes along with non-Gaussian and heteroscedastic noises.<n>It simultaneously infers the number of regimes and their corresponding indices and learns each regime's Directed Acyclic Graph.
  arXiv  Detail & Related papers  (2025-06-20T15:12:43Z)
• Detecting and Identifying Selection Structure in Sequential Data [53.24493902162797]
  We argue that the selective inclusion of data points based on latent objectives is common in practical situations, such as music sequences. We show that selection structure is identifiable without any parametric assumptions or interventional experiments. We also propose a provably correct algorithm to detect and identify selection structures as well as other types of dependencies.
  arXiv  Detail & Related papers  (2024-06-29T20:56:34Z)
• Causal Temporal Regime Structure Learning [49.77103348208835]
  We present CASTOR, a novel method that concurrently learns the Directed Acyclic Graph (DAG) for each regime.<n>We establish the identifiability of the regimes and DAGs within our framework.<n>Experiments show that CASTOR consistently outperforms existing causal discovery models.
  arXiv  Detail & Related papers  (2023-11-02T17:26:49Z)
• Probabilistic Learning of Multivariate Time Series with Temporal Irregularity [21.361823581838355]
  Real-world time series often suffer from temporal irregularities, including nonuniform intervals and misaligned variables.<n>We propose an end-to-end framework that models temporal irregularities while capturing the joint distribution of variables at arbitrary continuous-time points.
  arXiv  Detail & Related papers  (2023-06-15T14:08:48Z)
• Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models [61.10851158749843]
  Key insights can be obtained by discovering lead-lag relationships inherent in the data. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models.
  arXiv  Detail & Related papers  (2023-05-11T10:30:35Z)
• eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract) [0.3314882635954752]
  We present a constraint-based CD approach for autocorrelated and non-stationary time series data (eCDANs) eCDANs can detect lagged and contemporaneous causal relationships along with temporal changes. Experiments on synthetic and real-world data show that eCDANs can identify time influence and outperform the baselines.
  arXiv  Detail & Related papers  (2023-03-06T01:59:45Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments [0.0]
  Causal discovery offers a crucial functionality in scientific discovery using artificial intelligence. It became apparent that many real-world temporal observations are nonlinearly related to each other. We show that our method outperforms state-of-the-art causal discovery methods when the observations are restricted by time and are nonlinearly related.
  arXiv  Detail & Related papers  (2021-06-01T22:42:51Z)
• Consistency of mechanistic causal discovery in continuous-time using Neural ODEs [85.7910042199734]
  We consider causal discovery in continuous-time for the study of dynamical systems. We propose a causal discovery algorithm based on penalized Neural ODEs.
  arXiv  Detail & Related papers  (2021-05-06T08:48:02Z)
• Disentangling Observed Causal Effects from Latent Confounders using Method of Moments [67.27068846108047]
  We provide guarantees on identifiability and learnability under mild assumptions. We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions.
  arXiv  Detail & Related papers  (2021-01-17T07:48:45Z)
• Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference [50.029659413650194]
  Existing methods either require expensive feature engineering or are incapable of modeling the global dependencies among the events. In this paper, we propose a novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference.
  arXiv  Detail & Related papers  (2020-12-16T08:23:03Z)
• Large-scale nonlinear Granger causality: A data-driven, multivariate approach to recovering directed networks from short time-series data [0.0]
  We introduce a large-scale Granger Causality (lsNGC) approach for inferring causal interactions between system components. lsNGC identifies casual relations with no explicit a priori assumptions on functional interdependence between component time-series. We extensively study the ability of lsNGC to recovering network structure from two-node to thirty-four node chaotic time-series systems.
  arXiv  Detail & Related papers  (2020-09-10T06:27:57Z)
• Independence Testing for Temporal Data [14.25244839642841]
  A fundamental question is whether two time-series are related or not. Existing approaches often have limitations, such as relying on parametric assumptions. This paper introduces the temporal dependence statistic with block permutation to test independence between temporal data.
  arXiv  Detail & Related papers  (2019-08-18T17:19:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.