Awesome Linear & Patch Models

Key papers

• Kolmogorov-Arnold Networks (KANs) for Time Series Analysis (2024)
  Cristian J. Vaca-Rubio et al.
  10.12
• DBLoss: Decomposition-based Loss Function for Time Series Forecasting (2025)
  Xiangfei Qiu et al.
  9.74
• A Multi-Task End-to-End Multivariate Long-Sequence Time Series Prediction Model for Load Forecasting (2026)
  Ziyuan Zhang et al.
  9.50
• Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective (2025)
  Xingjian Wu et al.
  9.31
• CrossLinear: Plug-and-Play Cross-Correlation Embedding for Time Series Forecasting with Exogenous Variables (2025)
  Pengfei Zhou et al.
  8.60
• TKAN: Temporal Kolmogorov-Arnold Networks (2024)
  Remi Genet and Hugo Inzirillo
  8.02
• TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation (2025)
  Juntong Ni et al.
  8.01
• A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention (2025)
  Tao Yang et al.
  7.88
• FilterTS: Comprehensive Frequency Filtering for Multivariate Time Series Forecasting (2025)
  Yulong Wang and Yushuo Liu and Xiaoyi Duan and Kai Wang
  7.80
• FTS: A Framework to Find a Faithful TimeSieve (2024)
  Songning Lai et al.
  7.27
• Structured Linear CDEs: Maximally Expressive and Parallel-in-Time Sequence Models (2025)
  Benjamin Walker and Lingyi Yang and Nicola Muca Cirone and Cristopher Salvi and Terry Lyons
  7.19
• Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorization (2022)
  Xinyu Chen et al.
  7.16
• SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting (2024)
  Xiongxiao Xu et al.
  7.08
• 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.
  6.80
• TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting (2025)
  Shibo Feng et al.
  6.47
• Nearest Neighbor Multivariate Time Series Forecasting (2025)
  Huiliang Zhang et al.
  6.34
• Forecasting S&P 500 Using LSTM Models (2025)
  Prashant Pilla and Raji Mekonen
  6.12
• TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting (2025)
  Mingyuan Xia et al.
  6.04
• A Survey on Deep Learning based Time Series Analysis with Frequency Transformation (2023)
  Kun Yi and Qi Zhang and Wei Fan and Longbing Cao and Shoujin Wang and Guodong Long and Liang Hu and Hui He and Qingsong Wen and Hui Xiong
  5.91
• Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks (2024)
  Zijian Zhang et al.
  5.91
• TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting (2025)
  Vladyslav Moroshan and Julien Siems and Arber Zela and Timur Carstensen and Frank Hutter
  5.68
• TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding (2025)
  Kuiye Ding and Fanda Fan and Chunyi Hou and Zheya Wang and Lei Wang and Zhengxin Yang and Jianfeng Zhan
  5.63
• Unlocking the Power of LSTM for Long Term Time Series Forecasting (2024)
  Yaxuan Kong et al.
  5.57
• Are KANs Effective for Multivariate Time Series Forecasting? (2024)
  Xiao Han et al.
  5.57
• Using Pre-trained LLMs for Multivariate Time Series Forecasting (2025)
  Malcolm L. Wolff et al.
  5.54
• CT-PatchTST: Channel-Time Patch Time-Series Transformer for Long-Term Renewable Energy Forecasting (2025)
  Kuan Lu et al.
  5.54
• Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting (2024)
  Qi Xiong et al.
  5.46
• FreDF: Learning to Forecast in the Frequency Domain (2024)
  Hao Wang et al.
  5.23
• Timer-XL: Long-Context Transformers for Unified Time Series Forecasting (2024)
  Yong Liu et al.
  5.02
• Towards Lightweight Time Series Forecasting: a Patch-wise Transformer with Weak Data Enriching (2025)
  Meng Wang et al.
  4.76
• Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach (2025)
  Adam Nelson-Archer et al.
  4.58
• DisMS-TS: Eliminating Redundant Multi-Scale Features for Time Series Classification (2025)
  Zhipeng Liu et al.
  4.58
• A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series Forecasting (2025)
  Jiankai Zheng and Liang Xie
  4.53
• Modular Deep Learning for Multivariate Time-Series: Decoupling Imputation and Downstream Tasks (2024)
  Joseph Arul Raj et al.
  4.52
• Disentangled Parameter-Efficient Linear Model for Long-Term Time Series Forecasting (2024)
  Yuang Zhao et al.
  4.52
• FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance (2026)
  Jiaze Sun et al.
  4.39
• PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks (2026)
  Jichao Li et al.
  4.39
• Does Normalization Choice Matter for Causal Large Time-Series Models? (2026)
  Samy-Melwan Vilhes (LMAC) et al.
  4.39
• CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting (2026)
  Yosuke Yamaguchi et al.
  4.39
• Multiple cyclicity and Wavelet Decomposition with Channel Correlation for Long-term Time Series Forecasting (2026)
  Bin Wang et al.
  4.39
• Nonlinear and Heavy-Tailed Predictability in Transition-Energy Financial Markets (2026)
  Kpante Emmanuel Gnandi (INSA Toulouse) et al.
  4.33
• A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges (2024)
  Jongseon Kim et al.
  4.14
• Multi-period Learning for Financial Time Series Forecasting (2025)
  Xu Zhang and Zhengang Huang and Yunzhi Wu and Xun Lu and Erpeng Qi and Yunkai Chen and Zhongya Xue and Qitong Wang and Peng Wang and Wei Wang
  4.14
• Tackling Time-Series Forecasting Generalization via Mitigating Concept Drift (2025)
  Zhiyuan Zhao et al.
  4.09
• SwiftTS: A Swift Selection Framework for Time Series Pre-trained Models via Multi-task Meta-Learning (2025)
  Tengxue Zhang et al.
  4.09
• From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting (2025)
  Xilin Dai et al.
  4.03
• Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models (2025)
  Kun Feng et al.
  4.03
• Wavelet Mixture of Experts for Time Series Forecasting (2025)
  Zheng Zhou et al.
  3.97
• Long Input Sequence Network for Long Time Series Forecasting (2024)
  Chao Ma et al.
  3.92
• A tensor network approach for chaotic time series prediction (2025)
  Rodrigo Mart\'inez-Pe\~na et al.
  3.81
• TimeCapsule: Solving the Jigsaw Puzzle of Long-Term Time Series Forecasting with Compressed Predictive Representations (2025)
  Yihang Lu et al.
  3.75
• Addressing Challenges in Time Series Forecasting: A Comprehensive Comparison of Machine Learning Techniques (2025)
  Seyedeh Azadeh Fallah Mortezanejad et al.
  3.70
• Time-Series Forecasting via Topological Information Supervised Framework with Efficient Topological Feature Learning (2025)
  ZiXin Lin et al.
  3.70
• Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning (2025)
  Minbo Ma et al.
  3.64
• CryptoMamba: Leveraging State Space Models for Accurate Bitcoin Price Prediction (2025)
  Mohammad Shahab Sepehri et al.
  3.59
• xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition (2024)
  Artyom Stitsyuk and Jaesik Choi
  3.53
• Time Series Analysis in Machine Learning (2026)
  Antonio Pagliaro et al.
  3.51
• Graph Deep Learning for Time Series Forecasting (2023)
  Andrea Cini et al.
  3.42
• TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation (2024)
  Daoyu Wang et al.
  3.42