Key papers

• Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data (2024)
  YongKyung Oh et al.
  4.93
• A tensor network approach for chaotic time series prediction (2025)
  Rodrigo Mart\'inez-Pe\~na et al.
  4.82
• On the Diverse Dynamical Behaviors Arising in Deep Linear Transformers (2026)
  Sixu Li et al.
  4.39
• Recurrent Stochastic Configuration Networks for Temporal Data Analytics (2024)
  Dianhui Wang and Gang Dang
  4.37
• Multi-Rate Mixture of Experts for Accelerating Liquid Neural Network Training (2026)
  Shilong Zong and Almuatazbellah Boker and Hoda Eldardiry
  4.33
• Comprehensive Review of Neural Differential Equations for Time Series Analysis (2025)
  YongKyung Oh et al.
  3.59
• Enhancing Continuous Time Series Modelling with a Latent ODE-LSTM Approach (2023)
  C. Coelho et al.
  3.25
• SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms (2025)
  R\'emi Genet and Hugo Inzirillo
  2.65
• Rapid training of quantum recurrent neural networks (2022)
  Micha{\l} Siemaszko et al.
  2.53
• Backpropagation on Dynamical Networks (2022)
  Eugene Tan et al.
  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
• Physics-Informed Recurrent Architecture with Embedded Thermodynamic Dynamics for Robust Sequence Modeling (2026)
  Zafer Yiğit et al.
  1.94
• Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift (2026)
  Zhaobo Hu and Vincent Gauthier and Mehdi Naima
  1.83
• 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
• Nansde-net: A neural sde framework for generating time series with memory (2026)
  Hiromu Ozai and Kei Nakagawa
  1.72
• Continuous-Time Piecewise-Linear Recurrent Neural Networks (2026)
  Alena Br\"andle et al.
  1.72
• Grasynda: Graph-based Synthetic Time Series Generation (2026)
  Luis Amorim et al.
  1.67
• Non-negative DAG Learning from Time-Series Data (2025)
  Samuel Rey et al.
  1.61
• Neural CDEs as Correctors for Learned Time Series Models (2025)
  Muhammad Bilal Shahid et al.
  1.61
• Random Controlled Differential Equations (2025)
  Francesco Piatti et al.
  1.61
• Cross-Population Amplitude Coupling in High-Dimensional Oscillatory Neural Time Series (2021)
  Heejong Bong et al.
  1.60
• Modeling Time Series Dynamics with Fourier Ordinary Differential Equations (2025)
  Muhao Guo et al.
  1.50
• Graph Neural Networks with Diversity-aware Neighbor Selection and Dynamic Multi-scale Fusion for Multivariate Time Series Forecasting (2025)
  Jingqi Xu et al.
  1.44
• Neural Predictive Control to Coordinate Discrete- and Continuous-Time Models for Time-Series Analysis with Control-Theoretical Improvements (2025)
  Haoran Li and Muhao Guo and Yang Weng and Hanghang Tong
  1.39
• Optimizing Basis Function Selection in Constructive Wavelet Neural Networks and Its Applications (2025)
  Dunsheng Huang et al.
  1.33
• A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes (2025)
  Meiliang Liu et al.
  1.33
• Neural Ordinary Differential Equations for Learning and Extrapolating System Dynamics Across Bifurcations (2025)
  Eva van Tegelen et al.
  1.33
• Neural Functions for Learning Periodic Signal (2025)
  Woojin Cho et al.
  1.28
• Inference of hidden common driver dynamics by anisotropic self-organizing neural networks (2025)
  Zsigmond Benk\H{o} et al.
  1.17
• Generalized network autoregressive modelling of longitudinal networks with application to presidential elections in the USA (2025)
  Guy Nason et al.
  1.11
• CoRe: Coherency Regularization for Hierarchical Time Series (2025)
  Rares Cristian et al.
  1.06
• Kolmogorov-Arnold Networks for Time Series Granger Causality Inference (2025)
  Meiliang Liu et al.
  1.00
• When are dynamical systems learned from time series data statistically accurate? (2024)
  Jeongjin Park and Nicole Yang and Nisha Chandramoorthy
  0.94
• State Estimation Using Sparse DEIM and Recurrent Neural Networks (2024)
  Mohammad Farazmand
  0.89
• Latent Space Energy-based Neural ODEs (2024)
  Sheng Cheng et al.
  0.83
• NOMADS: Non-Markovian Optimization-based Modeling for Approximate Dynamics with Spatially-homogeneous Memory (2024)
  Ryoji Anzaki and Kazuhiro Sato
  0.83
• DeNOTS: Stable Deep Neural ODEs for Time Series (2024)
  Ilya Kuleshov et al.
  0.78
• A novel Reservoir Architecture for Periodic Time Series Prediction (2024)
  Zhongju Yuan et al.
  0.61
• Neural Chronos ODE: Unveiling Temporal Patterns and Forecasting Future and Past Trends in Time Series Data (2023)
  C.Coelho et al.
  0.06
• Deep Learning for Music (2016)
  Allen Huang et al.
  —
• Training Echo State Networks with Regularization through Dimensionality Reduction (2016)
  Sigurd L{\o}kse et al.
  —
• Surprisal-Driven Feedback in Recurrent Networks (2016)
  Kamil M Rocki
  —
• Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences (2016)
  Daniel Neil et al.
  —
• Marked Temporal Dynamics Modeling based on Recurrent Neural Network (2017)
  Yongqing Wang et al.
  —
• A time series distance measure for efficient clustering of input output signals by their underlying dynamics (2017)
  Oliver Lauwers et al.
  —
• Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks (2017)
  Shuai Xiao et al.
  —
• Reservoir Computing on the Hypersphere (2017)
  M. Andrecut
  —
• Emulating dynamic non-linear simulators using Gaussian processes (2018)
  Hossein Mohammadi et al.
  —
• Learning Causally-Generated Stationary Time Series (2018)
  Wessel Bruinsma and Richard E. Turner
  —
• Efficient Recurrent Neural Networks using Structured Matrices in FPGAs (2018)
  Zhe Li et al.
  —
• Deep Generative Networks For Sequence Prediction (2018)
  Markus Beissinger
  —
• Complex Gated Recurrent Neural Networks (2018)
  Moritz Wolter and Angela Yao
  —
• Learning The Sequential Temporal Information with Recurrent Neural Networks (2018)
  Pushparaja Murugan
  —
• Rapid Time Series Prediction with a Hardware-Based Reservoir Computer (2018)
  Daniel Canaday et al.
  —
• Mod-DeepESN: Modular Deep Echo State Network (2018)
  Zachariah Carmichael et al.
  —
• Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach (2018)
  Alexander Lin et al.
  —
• Deep Poisson gamma dynamical systems (2018)
  Dandan Guo et al.
  —
• Mixed Membership Recurrent Neural Networks (2018)
  Ghazal Fazelnia et al.
  —
• Comparison between DeepESNs and gated RNNs on multivariate time-series prediction (2018)
  Claudio Gallicchio et al.
  —
• Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference (2019)
  Xinyu Hu et al.
  —