Key papers

• Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes (2026)
  Yiqun Zhang et al.
  4.39
• Geometric mean-based pairwise comparison method with the reference values -- statistical approach (2026)
  Konrad Ku{\l}akowski et al.
  4.39
• MixCIT: A Kernel Based Local-Polynomial Debiased Test for Conditional Independence on Mixed-Type Data (2026)
  Mengxiao Gao et al.
  3.51
• Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics (2026)
  Vignesh Tirukkonda et al.
  3.51
• The Tractability Landscape of Sampling with Inexact Scores (2026)
  Anming Gu et al.
  3.51
• Boundary-Adapted PINNs for Elliptic Dirichlet Problems: $H^2(\Omega)$ A Priori Error Bounds with Application to Mean Escape Time Computation (2026)
  Nathanael Tepakbong et al.
  3.51
• Conformal Inference for Time Series over Graphs (2025)
  Sonakshi Dua et al.
  3.10
• Significance-First Splitting: Aligning Treatment Heterogeneity Detection with Honest Estimation (2026)
  Pantelis Z. Hadjipantelis et al.
  2.00
• Conformal Prediction Algorithms for Time Series Forecasting: Methods and Benchmarking (2026)
  Andro Sabashvili
  1.67
• On Statistical Inference for High-Dimensional Binary Time Series (2025)
  Dehao Dai et al.
  1.61
• Learning Conditional Independence Differential Graphs From Time-Dependent Data (2025)
  Jitendra K Tugnait
  1.61
• A Gentle Introduction to Conformal Time Series Forecasting (2025)
  M. Stocker et al.
  1.56
• Predictive inference for time series: why is split conformal effective despite temporal dependence? (2025)
  Rina Foygel Barber et al.
  1.50
• Fractal approach towards power-law coherency to measure cross-correlations between time series (2016)
  Ladislav Kristoufek
  —
• Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series (2017)
  Ruben Loaiza-Maya and Michael Stanley Smith
  —
• Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets (2020)
  Jakob Runge
  —
• A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence (2020)
  S. O. Tickle and I. A. Eckley and P. Fearnhead
  —
• Time series models with infinite-order partial copula dependence (2021)
  Martin Bladt and Alexander J. McNeil
  —
• On Sparse High-Dimensional Graphical Model Learning For Dependent Time Series (2021)
  Jitendra K. Tugnait
  —
• Characterization of causal ancestral graphs for time series with latent confounders (2021)
  Andreas Gerhardus
  —
• Sparse-Group Log-Sum Penalized Graphical Model Learning For Time Series (2022)
  Jitendra K Tugnait
  —
• Inferring extended summary causal graphs from observational time series (2022)
  Charles K. Assaad et al.
  —
• A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting (2022)
  Martim Sousa et al.
  —
• Sequential Predictive Conformal Inference for Time Series (2022)
  Chen Xu et al.
  —
• Detection and Estimation of Structural Breaks in High-Dimensional Functional Time Series (2023)
  Degui Li et al.
  —
• Testing for the Markov Property in Time Series via Deep Conditional Generative Learning (2023)
  Yunzhe Zhou and Chengchun Shi and Lexin Li and Qiwei Yao
  —
• Change-points analysis for generalized integer-valued autoregressive model via minimum description length principle (2023)
  Danshu Sheng et al.
  —
• Nonhomogeneous hidden semi-Markov models for toroidal data (2023)
  Francesco Lagona and Marco Mingione
  —
• Conformal prediction for multi-dimensional time series by ellipsoidal sets (2024)
  Chen Xu et al.
  —