Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions

• URL: http://arxiv.org/abs/2605.03061v2
• Date: Thu, 07 May 2026 06:20:57 GMT
• Title: Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions
• Abstract summary: We introduce Dynamic Vine Copulas (DVC), a temporal vine-copula framework for estimating and diagnosing sequence-wide non-Gaussian dependence.<n>The main diagnostic is a comparison between a full vine and its matched 1-truncated version, which separates flexible first-tree pairwise dependence from evidence contributed by higher-tree conditional terms.<n>DVC provides a flexible temporal copula model and an interpretable test of whether temporal dependence changes are pairwise or conditional.
• Score: 47.562560779723356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time-varying dependence is often modeled with dynamic correlations or Gaussian graphical models, but multivariate systems can change through tail behavior, asymmetry, or conditional structure even when correlations are nearly stable. We introduce Dynamic Vine Copulas (DVC), a temporal vine-copula framework for estimating and diagnosing sequence-wide non-Gaussian dependence. DVC fixes a chosen vine factorization for comparability; the framework applies to C-, D-, and R-vines, and our experiments use fixed-root-order C-vines. Pair-copula states evolve through smooth parameter trajectories or temporally regularized family-switching paths. The main diagnostic is a held-out comparison between a full vine and its matched 1-truncated version, which separates flexible first-tree pairwise dependence from evidence contributed by higher-tree conditional terms. At the population level, under a correct fixed vine and the simplifying assumption, this contrast equals the higher-tree component of a vine total-correlation decomposition; in finite samples, it is a predictive diagnostic. In controlled benchmarks, DVC detects Student-t degrees-of-freedom changes, Clayton-to-Gumbel switches, and recurrent conditional-interaction episodes missed or conflated by Gaussian dynamic baselines. The higher-tree score remains near zero in pairwise-only regimes and rises during conditional-interaction regimes. On Allen Visual Behavior Neuropixels data, DVC identifies a reproducible time-indexed higher-tree signal that is positive across held-out splits and vanishes under a decorrelated null, indicating simultaneous cross-area dependence. DVC therefore provides a flexible temporal copula model and an interpretable test of whether temporal dependence changes are pairwise or conditional.

Related papers

• When Interference Graphs Evolve: Doubly Robust Estimation of Dynamic Peer Effects [1.049126606580198]
  We introduce a controlled contrast framework that indexes potential outcomes by own treatment, temporally aggregated peer exposure, and a post-assignment evolution summary.<n>We develop the Dynamic Network Doubly Robust estimator, DynaNet-DR, which combines a temporally factorized propensity with normalized augmentation.
  arXiv  Detail & Related papers  (2026-08-27T14:32:40Z)
• Disentangling Continuous-Time Latent Dynamics: Identifiability of Latent SDEs via Diffusion Shifts [60.95873409673775]
  identifiability in continuous-time latent differential equation models remains largely open.<n>We study additive-noise latent SDEs observed through an unknown nonlinear diffeomorphism.<n>We propose a two-stage estimator for latent disentanglement and optional graph recovery.
  arXiv  Detail & Related papers  (2026-06-26T16:18:28Z)
• Distance-Aware Joint Spatio-Temporal Graph Contrastive Learning for Major Depressive Disorder Diagnosis [14.96995835279229]
  Major depressive disorder (MDD) is a common condition whose accurate diagnosis from resting-state functional magnetic resonance imaging (rs-fMRI) remains difficult.<n> Dynamic functional connectivity (DFC) captures time-varying interactions among brain regions and provides rich-temporal correlation, yet DFC-based methods face three limitations.
  arXiv  Detail & Related papers  (2026-05-22T06:12:31Z)
• Evaluating Temporal and Structural Anomaly Detection Paradigms for DDoS Traffic [0.046603287532620725]
  Unsupervised anomaly detection is widely used to detect Distributed Denial-of-Service (DDoS) attacks in cloud-native 5G networks.<n>Most studies assume a fixed traffic representation, either temporal or structural, without validating which feature space best matches the data.<n>We propose a lightweight decision framework that prioritizes temporal or structural features before training, using two diagnostics.
  arXiv  Detail & Related papers  (2026-04-17T12:59:48Z)
• From GEV to ResLogit: Spatially Correlated Discrete Choice Models for Pedestrian Movement Prediction [1.8262547855491453]
  We formulate the pedestrian next step choice as a spatial discrete choice defined by a grid of speed adjustment and heading change.<n>In dense, high frequency spatial choice sets, learning based residual corrections can capture proximity induced correlation more effectively than analyst specified GEV nesting structures.
  arXiv  Detail & Related papers  (2026-03-01T23:50:33Z)
• SMKC: Sketch Based Kernel Correlation Images for Variable Cardinality Time Series Anomaly Detection [0.0]
  In operational environments, monitoring systems frequently experience sensor churn.<n>We propose SMKC, a framework that decouples the dynamic input structure from the anomaly detector.<n>We find that a detector using random projections and nearest neighbors on the SMKC representation performs competitively with fully trained baselines.
  arXiv  Detail & Related papers  (2026-01-28T21:15:11Z)
• Volatility in Certainty (VC): A Metric for Detecting Adversarial Perturbations During Inference in Neural Network Classifiers [0.5793804025420254]
  Adversarial robustness remains a critical challenge in deploying neural network classifiers.<n>This paper investigates textitVolatility in Certainty (VC), a label-free metric that quantifies irregularities in model confidence.
  arXiv  Detail & Related papers  (2025-11-14T19:51:04Z)
• Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection [39.049577655194746]
  This paper proposes Causality-Aware contrastive learning for RObust multivariate Time-Series (CAROTS)<n>CAROTS employs two data augmentors to obtain causality-preserving and -disturbing samples that serve as a wide range of normal variations and synthetic anomalies.<n>With causality-preserving and -disturbing samples as positives and negatives, CAROTS performs contrastive learning to train an encoder whose latent space separates normal and abnormal samples based on causality.
  arXiv  Detail & Related papers  (2025-06-04T13:57:11Z)
• VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting [1.5165632546654102]
  We propose Variable Correlation Transformer (VCformer) to mine the correlations among variables. VCA calculates and integrates the cross-correlation scores corresponding to different lags between queries and keys. Inspired by Koopman dynamics theory, we also develop Koopman Temporal Detector (KTD) to better address the non-stationarity in time series.
  arXiv  Detail & Related papers  (2024-05-19T07:39:22Z)
• Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data [50.84488941336865]
  We propose a novel method called Fully- Spatial-Temporal Graph Neural Network (FC-STGNN) For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances. For graph convolution, we devise FC graph convolution with a moving-pooling GNN layer to effectively capture the ST dependencies for learning effective representations.
  arXiv  Detail & Related papers  (2023-09-11T08:44:07Z)
• Graph-Aware Contrasting for Multivariate Time-Series Classification [50.84488941336865]
  Existing contrastive learning methods mainly focus on achieving temporal consistency with temporal augmentation and contrasting techniques. We propose Graph-Aware Contrasting for spatial consistency across MTS data. Our proposed method achieves state-of-the-art performance on various MTS classification tasks.
  arXiv  Detail & Related papers  (2023-09-11T02:35:22Z)
• Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection [67.60791405198063]
  We propose a correlation-aware spatial-temporal graph learning (termed CST-GL) for time series anomaly detection. CST-GL explicitly captures the pairwise correlations via a multivariate time series correlation learning module. A novel anomaly scoring component is further integrated into CST-GL to estimate the degree of an anomaly in a purely unsupervised manner.
  arXiv  Detail & Related papers  (2023-07-17T11:04:27Z)
• 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)
• Deep Explicit Duration Switching Models for Time Series [84.33678003781908]
  We propose a flexible model that is capable of identifying both state- and time-dependent switching dynamics. State-dependent switching is enabled by a recurrent state-to-switch connection. An explicit duration count variable is used to improve the time-dependent switching behavior.
  arXiv  Detail & Related papers  (2021-10-26T17:35:21Z)
• 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)

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.