Key papers

• TKAN: Temporal Kolmogorov-Arnold Networks (2024)
  Remi Genet and Hugo Inzirillo
  8.02
• A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention (2025)
  Tao Yang et al.
  7.82
• TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting (2025)
  Shibo Feng et al.
  6.41
• Nearest Neighbor Multivariate Time Series Forecasting (2025)
  Huiliang Zhang et al.
  6.28
• Time-Series Learning for Proactive Fault Prediction in Distributed Systems with Deep Neural Structures (2025)
  Yang Wang et al.
  5.35
• 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
• Enhancing Continuous Time Series Modelling with a Latent ODE-LSTM Approach (2023)
  C. Coelho et al.
  3.25
• Hybrid quantum recurrent neural network for remaining useful life prediction (2025)
  Olga Tsurkan et al.
  2.76
• Differential Machine Learning for Time Series Prediction (2025)
  Akash Yadav and Eulalia Nualart
  2.71
• SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms (2025)
  R\'emi Genet and Hugo Inzirillo
  2.65
• Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data (2025)
  Harsh Poonia et al.
  2.65
• Rapid training of quantum recurrent neural networks (2022)
  Micha{\l} Siemaszko 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
• 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
• Tuning the burn-in phase in training recurrent neural networks improves their performance (2026)
  Julian D. Schiller and Malte Heinrich and Victor G. Lopez and Matthias A. M\"uller
  1.72
• Hybrid Quantum Temporal Convolutional Networks (2026)
  Junghoon Justin Park et al.
  1.72
• SpikySpace: A Spiking State Space Model for Energy-Efficient Time Series Forecasting (2026)
  Kaiwen Tang et al.
  1.67
• MemKD: Memory-Discrepancy Knowledge Distillation for Efficient Time Series Classification (2026)
  Nilushika Udayangani et al.
  1.67
• COMET-SG1: Lightweight Autoregressive Regressor for Edge and Embedded AI (2026)
  Shakhyar Gogoi
  1.67
• Long-Sequence LSTM Modeling for NBA Game Outcome Prediction Using a Novel Multi-Season Dataset (2025)
  Charles Rios et al.
  1.61
• Trend Extrapolation for Technology Forecasting: Leveraging LSTM Neural Networks for Trend Analysis of Space Exploration Vessels (2025)
  Peng-Hung Tsai et al.
  1.61
• Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models (2025)
  Eliane Younes and Elie Hachem and Marc Bernacki
  1.56
• Forecasting Future DDoS Attacks Using Long Short Term Memory (LSTM) Model (2025)
  Kong Mun Yeen et al.
  1.44
• Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models (2025)
  Jonathan Teagan
  1.44
• ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting (2025)
  Myung Jin Kim et al.
  1.44
• Echo Flow Networks (2025)
  Hongbo Liu and Jia Xu
  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
• SpikeSTAG: Spatial-Temporal Forecasting via GNN-SNN Collaboration (2025)
  Bang Hu et al.
  1.39
• Adaptive Fine-Tuning via Pattern Specialization for Deep Time Series Forecasting (2025)
  Amal Saadallah et al.
  1.39
• Long-Sequence Memory with Temporal Kernels and Dense Hopfield Functionals (2025)
  Ahmed Farooq
  1.33
• Neural Functions for Learning Periodic Signal (2025)
  Woojin Cho et al.
  1.28
• MS-DFTVNet:A Long-Term Time Series Prediction Method Based on Multi-Scale Deformable Convolution (2025)
  Chenghan Li et al.
  1.28
• Approximately Equivariant Recurrent Generative Models for Quasi-Periodic Time Series with a Progressive Training Scheme (2025)
  Ruwen Fulek and Markus Lange-Hegermann
  1.22
• IRNN: Innovation-driven Recurrent Neural Network for Time-Series Data Modeling and Prediction (2025)
  Yifan Zhou et al.
  1.22
• An Efficient Continual Learning Framework for Multivariate Time Series Prediction Tasks with Application to Vehicle State Estimation (2025)
  Arvin Hosseinzadeh et al.
  1.11
• Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory (2025)
  Sascha Marton et al.
  1.06
• BRATI: Bidirectional Recurrent Attention for Time-Series Imputation (2025)
  Armando Collado-Villaverde et al.
  1.00
• Time series forecasting for multidimensional telemetry data using GAN and BiLSTM in a Digital Twin (2025)
  Joao Carmo de Almeida Neto et al.
  1.00
• Overcoming Data Limitations in Internet Traffic Forecasting: LSTM Models with Transfer Learning and Wavelet Augmentation (2024)
  Sajal Saha et al.
  0.83
• A Policy Gradient-Based Sequence-to-Sequence Method for Time Series Prediction (2024)
  Qi Sima et al.
  0.67
• Neural Chronos ODE: Unveiling Temporal Patterns and Forecasting Future and Past Trends in Time Series Data (2023)
  C.Coelho et al.
  0.06
• Comparing Time and Frequency Domain for Audio Event Recognition Using Deep Learning (2016)
  Lars Hertel et al.
  —
• 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
  —
• End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks (2016)
  Timothy J. O'Shea et al.
  —
• Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences (2016)
  Daniel Neil et al.
  —
• Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks (2016)
  Eder Santana et al.
  —
• Deep Symbolic Representation Learning for Heterogeneous Time-series Classification (2016)
  Shengdong Zhang and Soheil Bahrampour and Naveen Ramakrishnan and Mohak Shah
  —
• Predicting Process Behaviour using Deep Learning (2016)
  Joerg Evermann et al.
  —
• Marked Temporal Dynamics Modeling based on Recurrent Neural Network (2017)
  Yongqing Wang et al.
  —
• Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks (2017)
  Shuai Xiao et al.
  —
• Compressing Recurrent Neural Network with Tensor Train (2017)
  Andros Tjandra et al.
  —
• DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction (2017)
  Xingyuan Dai et al.
  —
• Time-Dependent Representation for Neural Event Sequence Prediction (2017)
  Yang Li et al.
  —
• Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks (2017)
  Narendhar Gugulothu et al.
  —
• LSTM Fully Convolutional Networks for Time Series Classification (2017)
  Fazle Karim et al.
  —
• House Price Prediction Using LSTM (2017)
  Xiaochen Chen et al.
  —
• Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach (2017)
  Kasun Bandara et al.
  —