Self-Supervised Time Series Representation Learning

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Overview

Self-supervised time series representation learning studies how to learn useful features from sequences without relying on extensive labels, which are often costly or unavailable. Common approaches include contrastive learning between related views, masked reconstruction, generative prediction, and joint-embedding predictive architectures (JEPAs), implemented with convolutional networks, Transformers, or state-space models. These representations are tested across tasks such as activity recognition, forecasting, anomaly detection, and clinical analysis of ECG, EEG, and wearable signals, often using frozen linear probes, fine-tuning, and cross-dataset transfer. A central open question is which objectives and inductive biases help each task: gains vary across datasets and architectures, while invariance to nuisance variation can also erase meaningful changes over time.

Latest papers

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146 papers23 papers in the last four weeks, up 77% on the four weeks before. 0.1% of all new papers.

  1. Can Natural Image Autoencoders Compactly Tokenize fMRI Volumes for Long-Range Dynamics Modeling?

    Apr 4, 2026Peter Yongho Kim, Juhyeon Park, Jungwoo Park +4fMRI Representation LearningLong-Context Modeling

    ECE, Seoul National University · IPAI, Seoul National University · BCS, Seoul National University +2

  2. A General Framework for Generative Self-supervised Learning in Non-invasive Estimation of Physiological Parameters Using Photoplethysmography

    Apr 3, 2026Zexing Zhang, Huimin Lu, Songzhe Ma +4Self-Supervised Time Series Representation LearningPhotoplethysmography

    School of Computer Science and Engineering, Changchun University of Technology, Jilin, Changchun, 130102, China · Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Jilin, Changchun, 130012, China · Smart Health Joint Innovation Laboratory for the New Generation of AI, Jilin, Changchun, 130102, China

  3. Temporal Contrastive Transformer for Financial Crime Detection: Self-Supervised Sequence Embeddings via Predictive Contrastive Coding

    Mar 31, 2026Danny Butvinik, Yonit Marcus, Nitzan Tal +1Self-Supervised Time Series Representation LearningPredictive Representation Learning

    NICE Actimize

  4. Foundation Model for Cardiac Time Series via Masked Latent Attention

    Mar 27, 2026Moritz Vandenhirtz, Samuel Ruipérez-Campillo, Simon Böhi +6Electrocardiogram Representation LearningElectrocardiogram Foundation Models

    Department of Computer Science, ETH Zurich, Switzerland · Department of Biomedical Engineering, University of Basel, Switzerland

  5. Contrastive Learning Boosts Deterministic and Generative Models for Weather Data

    Mar 25, 2026Nathan BaileyContrastive LearningGeospatial Representation Learning

    Supervised by Dr Sibo Cheng, Faculty of Engineering, Imperial College London and Dr Cesar Quilodran-Casas, The Grantham Institute for Climate Change, Faculty of Natural Sciences, Imperial College London.

  6. LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling

    Mar 19, 2026Danaé Broustail, Anna Tegon, Thorir Mar Ingolfsson +2EEG Foundation ModelsElectroencephalography

    Integrated Systems Laboratory, ETH Zürich, Zürich, Switzerland · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · DEI, University of Bologna, Bologna, Italy

  7. BoundAD: Boundary-Aware Negative Generation for Time Series Anomaly Detection

    Mar 18, 2026Xiancheng Wang, Lin Wang, Zhibo Zhang +2Time-Series Anomaly DetectionUnsupervised Anomaly Detection

    School of Ocean Engineering, Harbin Institute of Technology, West Wenhua Road, Weihai, 264209, Shandong, China. · Technical Center, Bogie Development Department, CRRC Qingdao Sifang Locomotive and Rolling Stock Co., Ltd., Jinhong East Road, Qingdao, 266111, Shandong, China.

  8. Laya: A LeJEPA Approach to EEG via Latent Prediction over Reconstruction

    Mar 17, 2026Saarang Panchavati, Uddhav Panchavati, Hiroki Nariai +2EEG Foundation ModelsSelf-Supervised Time Series Representation Learning

    Department of Medical Informatics UCLA Los Angeles, USA · Department of Cognitive Science UCSD San Diego, USA · David Geffen School of Medicine UCLA Mattel Children’s Hospital Los Angeles, USA

  9. Representation Learning for Spatiotemporal Physical Systems

    Mar 13, 2026Helen Qu, Rudy Morel, Michael McCabe +4Representation LearningScientific ML

    Flatiron Institute · Princeton University · Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM +1

  10. Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data

    Mar 12, 2026Keita Kayano, Takayuki Nishio, Daiki Yoda +2Wi-Fi SensingLearning with Missing Data

    School of Engineering, Institute of Science Tokyo, 2-12-1 Ookayama, Tokyo, 152-8550, Japan · Wireless Systems R&D Department, Infrastructure Systems R&D Center, Corporate Laboratory, Toshiba Corporation, 1, Komukai Toshiba-cho, Saiwai-ku, Kawasaki 212-8582, JAPAN

  11. Bio-Inspired Self-Supervised Learning for Wrist-worn Accelerometer Data

    Mar 11, 2026Prithviraj Tarale, Kiet Chu, Abhishek Varghese +4Human Activity RecognitionSelf-Supervised Time Series Representation Learning

    College of Information and Computer Sciences, University of Massachusetts, Amherst, United States · Stevens Institute of Technology, Hoboken, United States · Google Health, Seattle, United States +1

  12. Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos

    Mar 9, 2026Michelle Espranita Liman, Özgün Turgut, Alexander Müller +3Electrocardiogram Representation LearningMultimodal Learning

    Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Germany · Department of Cardiology, TUM University Hospital, Germany · Department of Computing, Imperial College London, UK +1

  13. UTICA: Multi-Objective Self-Distllation Foundation Model Pretraining for Time Series Classification

    Mar 2, 2026Yessin Moakher, Youssef Attia El Hili, Vasilii FeofanovSelf-Supervised Time Series Representation LearningTime-Series Foundation Models

    Ecole Polytechnique, France · Huawei Noah’s Ark Lab

  14. Interpretable Cross-Network Attention for Resting-State fMRI Representation Learning

    Feb 28, 2026Karanpartap Singh, Adam Turnbull, Mohammad Abbasi +3Brain Network AnalysisFunctional Connectivity

    Stanford University, Stanford, CA 94305

  15. RhythmBERT: A Self-Supervised Language Model Based on Latent Representations of ECG Waveforms for Heart Disease Detection

    Feb 26, 2026Xin Wang, Burcu Ozek, Aruna Mohan +3Electrocardiogram Representation LearningElectrocardiogram Foundation Models

    Virginia Tech, VA, USA · Walkky LLC, Boston, MA, USA · Northeastern University, Boston, MA, USA +1

  16. A general extractor for coherent patterns across time-frequency measurements

    Feb 23, 2026Nathaniel Chen, Kouroche Bouchiat, Peter Steiner +7Time-Frequency AnalysisSelf-Supervised Time Series Representation Learning

  17. LWM-Temporal: Sparse Spatio-Temporal Attention for Wireless Channel Representation Learning

    Feb 22, 2026Sadjad Alikhani, Akshay Malhotra, Shahab Hamidi-Rad +1Spatio-Temporal AttentionWireless Communications

  18. BrainRVQ: A High-Fidelity EEG Foundation Model via Dual-Domain Residual Quantization and Hierarchical Autoregression

    Feb 18, 2026Mingzhe Cui, Tao Chen, Yang Jiao +4EEG Foundation ModelsSelf-Supervised Time Series Representation Learning

    State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China · Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy · Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

  19. SleepMaMi: A Universal Sleep Foundation Model for Integrating Macro- and Micro-structures

    Feb 7, 2026Keondo Park, Younghoon Na, Yourim Choi +3Self-Supervised Time Series Representation Learning

    Graduate School of Data Science, Seoul National University, Seoul, South Korea · Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea · Obstructive Upper Airway Research (OUaR) Laboratory, Department of Pharmacology, Seoul National University College of Medicine, Seoul, Republic of Korea +1

  20. PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

    Feb 1, 2026Jinju Park, Seokho KangSelf-Supervised Time Series Representation LearningTime Series Representation Learning

    Department of Industrial Engineering, Sungkyunkwan University

  21. SingLEM: Single-Channel Large EEG Model

    Sep 22, 2025Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue +5Representation LearningSelf-Supervised Time Series Representation Learning

    Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Koganei-Shi 184–8588, Japan · Department of Electronics and Communication Engineering, National University of Mongolia, Ulaanbaatar, 14200 Mongolia

  22. EEGDM: Learning EEG Representation with Latent Diffusion Model

    Aug 28, 2025Shaocong Wang, Tong Liu, Yihan Li +6ElectroencephalographyEEG Signal Generation

    Tsinghua University · Tianjin University

  23. EEGDM: Label-Efficient EEG Representation Learning with Generative Diffusion Model

    Aug 13, 2025Jia Hong Puah, Sim Kuan Goh, Ziwei Zhang +6ElectroencephalographyEEG Seizure Detection

    School of Artificial Intelligence and Robotics, Xiamen University Malaysia · School of Energy and Chemical Engineering, Xiamen University Malaysia · Department of Biomedical Engineering, Universiti Malaya +3

  24. FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation

    Jul 22, 2025Pingyi Fan, Anbai Jiang, Shuwei Zhang +9Representation LearningSelf-Supervised Time Series Representation Learning

    Department of Electronic Engineering, Tsinghua University, Beijing, 100084, P. R. China · Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China · Huakong AI Plus Company Limited, Beijing, 100084, P. R. China +1

  25. Mantis: Lightweight Foundation Model for Time Series Classification

    Feb 21, 2025Vasilii Feofanov, Songkang Wen, Shifeng Xie +10Time Series ClassificationSelf-Supervised Time Series Representation Learning

    1. Huawei Noah’s Ark Lab · 3. Paris Cit´e University · 4. Helmholtz Munich +3

  26. Stochastic Optimal Control for Continuous-Time fMRI Representation Learning

    Feb 7, 2025Joonhyeong Park, Byoungwoo Park, Chang-Bae Bang +4Latent Dynamics ModelingSelf-Supervised Time Series Representation Learning

    KAIST · Yonsei University · EverEx +1