Key papers

• Kolmogorov-Arnold Networks (KANs) for Time Series Analysis (2024)
  Cristian J. Vaca-Rubio et al.
  10.12
• Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorization (2022)
  Xinyu Chen et al.
  7.16
• CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables (2024)
  Jiecheng Lu et al.
  6.84
• HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting (2025)
  Boyuan Li et al.
  6.75
• Nearest Neighbor Multivariate Time Series Forecasting (2025)
  Huiliang Zhang et al.
  6.28
• Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal Analysis (2018)
  Timothy Wong et al.
  6.24
• Robust time series generation via Schr\"odinger Bridge: a comprehensive evaluation (2025)
  Alexandre Alouadi et al.
  6.17
• Are KANs Effective for Multivariate Time Series Forecasting? (2024)
  Xiao Han et al.
  5.57
• Full Bayesian Reinforcement Learning via LF-IBIS (2026)
  Stefano Masini et al.
  5.01
• Disentangling Forced and Internal Climate Variability in Single Realizations using Dynamic Mode Decomposition with Control (2026)
  Nathan Mankovich et al.
  5.01
• Using binary silver labels in electronic health records-based computable phenotyping algorithms (2026)
  Shuhe Wang et al.
  5.01
• Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics (2026)
  Vignesh Tirukkonda et al.
  5.01
• The Tractability Landscape of Sampling with Inexact Scores (2026)
  Anming Gu et al.
  5.01
• Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data (2024)
  YongKyung Oh et al.
  4.93
• Laplacian Convolutional Representation for Traffic Time Series Imputation (2022)
  Xinyu Chen and Zhanhong Cheng and HanQin Cai and Nicolas Saunier and Lijun Sun
  4.79
• A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series Forecasting (2025)
  Jiankai Zheng and Liang Xie
  4.47
• PAC--Bayes Bounds on Quotient Parameter Spaces: Geometry-induced Implicit-Bias Priors (2026)
  Nicola Aladrah et al.
  4.39
• Elicitation without Backpropagation: Steering Model Behavior by Optimizing the Latent Posterior (2026)
  Garrett Baker et al.
  4.39
• Optimizing Regret (2026)
  Irene Aldridge
  4.39
• Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts (2026)
  Ali Maghami et al.
  4.39
• Provable diffusion-based posterior sampling for linear inverse problems via DDIM (2026)
  Yuchen Jiao et al.
  4.39
• Learning Temporal Causal Structure via Smooth Differentiable Optimization (2026)
  Tong Zhao et al.
  4.33
• SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting (2026)
  Xingsheng Chen et al.
  4.33
• Transformer for Times Series: an Application to the S&P500 (2024)
  Pierre Brugiere and Gabriel Turinici
  4.21
• Online time series prediction using feature adjustment (2025)
  Xiannan Huang et al.
  3.97
• Modeling Time-evolving Causality over Data Streams (2025)
  Naoki Chihara et al.
  3.59
• Assessment of Spatio-Temporal Predictors in the Presence of Missing and Heterogeneous Data (2023)
  Daniele Zambon et al.
  3.58
• 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
• Time Series Analysis in Machine Learning (2026)
  Antonio Pagliaro et al.
  3.45
• Graph Deep Learning for Time Series Forecasting (2023)
  Andrea Cini et al.
  3.42
• Conformal Inference for Time Series over Graphs (2025)
  Sonakshi Dua et al.
  3.10
• FreIE: Low-Frequency Spectral Bias in Neural Networks for Time-Series Tasks (2025)
  Jialong Sun et al.
  3.10
• Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks (2024)
  Rujia Shen et al.
  3.04
• MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting (2025)
  Binqing Wu et al.
  3.04
• Supervised Dynamic Dimension Reduction with Deep Neural Network (2025)
  Zhanye Luo et al.
  2.99
• Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality (2025)
  Grzegorz Dudek
  2.76
• Lightweight Channel-wise Dynamic Fusion Model: Non-stationary Time Series Forecasting via Entropy Analysis (2025)
  Tianyu Jia et al.
  2.71
• Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data (2025)
  Harsh Poonia et al.
  2.65
• $k$-Graph: A Graph Embedding for Interpretable Time Series Clustering (2025)
  Paul Boniol et al.
  2.65
• Hidden Markov Neural Networks (2020)
  Lorenzo Rimella and Nick Whiteley
  2.53
• A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification (2024)
  Flavio Corradini et al.
  2.49
• Linguistic Approach to Time Series Forecasting (2022)
  Dmytro Lande et al.
  2.26
• Nonstationary Time Series Forecasting via Unknown Distribution Adaptation (2024)
  Zijian Li et al.
  2.04
• Nested Spatio-Temporal Time Series Forecasting (2026)
  Yinghao Ai et al.
  1.89
• Generalizing Multi-Scale Time-Series Modeling with a Single Operator (2026)
  Cheonwoo Lee et al.
  1.89
• Causal Temporal Regime Structure Learning (2023)
  Abdellah Rahmani et al.
  1.87
• Time-Series Classification with Multivariate Statistical Dependence Features (2026)
  Yao Sun et al.
  1.83
• Auto-Configured Networks for Multi-Scale Multi-Output Time-Series Forecasting (2026)
  Yumeng Zha et al.
  1.83
• Forecasting Multivariate Time Series under Predictive Heterogeneity: A Validation-Driven Clustering Framework (2026)
  Ziling Ma et al.
  1.83
• Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift (2026)
  Zhaobo Hu and Vincent Gauthier and Mehdi Naima
  1.83
• SPaRSe-TIME: Saliency-Projected Low-Rank Temporal Modeling for Efficient and Interpretable Time Series Prediction (2026)
  K. A. Shahriar
  1.83
• Preserving Temporal Dynamics in Time Series Generation (2026)
  Ci Lin et al.
  1.83
• Learning with the Nash-Sutcliffe loss (2026)
  Hristos Tyralis et al.
  1.78
• Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting (2026)
  Yixin Wang et al.
  1.78
• Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series (2026)
  Madhurima Panja et al.
  1.78
• GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables (2026)
  Zhengyu Li et al.
  1.78
• Deep Distance Measurement Method for Unsupervised Multivariate Time Series Similarity Retrieval (2026)
  Susumu Naito et al.
  1.78
• Fast and Interpretable Autoregressive Estimation with Neural Network Backpropagation (2026)
  Ana\'isa Lucena and Ana Martins and Armando J. Pinho and S\'onia Gouveia
  1.78
• Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects (2026)
  Hao Wang et al.
  1.78
• Causal-INSIGHT: Probing Temporal Models to Extract Causal Structure (2026)
  Benjamin Redden et al.
  1.78