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. Are Seismic Foundation Models More Than Glorified Feature Extractors?

    Oct 27, 2026Laura Laurenti, David Liu, Brian Kulis +2Time-Series Foundation ModelsSelf-Supervised Time Series Representation Learning

    Institute of Geophysics, ETH Zurich · Swiss Seismological Service, ETH Zurich · Dept. of Computer Science, Boston University +2

  2. Self-Supervised Latent Action-Conditioned World Modeling for Time Series

    Oct 27, 2026Chia-Han Kuo, Chengshuai Zhao, Huan Liu +1Self-Supervised Time Series Representation LearningAction-Conditioned World Models

    Arizona State University

  3. Learning from Points and Patches: A Pre-trained Hybrid Attention Transformer for Time Series Analysis

    Oct 27, 2026Peiwang Tang, Cheng HE, Yupeng Ye +3Self-Supervised Time Series Representation LearningTime Series Transformers

    University of Science and Technology of China, Hefei, Anhui, China · State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China · Shanghai Black Wing Asset Management, Shanghai, China +1

  4. TransECG-SSL: Hybrid Self-Supervised Representation Learning for ECG Arrhythmia Classification

    Oct 26, 2026Ahtisham Ayyub, Fasih Haider, Muhammad Rehan Usman +3Electrocardiogram Representation LearningElectrocardiogram Classification

    School of Computer Science and Mathematics, Kingston University, London, United Kingdom · Usher Institute, University of Edinburgh, Edinburgh, United Kingdom

  5. YingLan: A Multimodal Ocean Foundation Model for Unified Representation Learning from Heterogeneous Observations

    Oct 21, 2026Sen Jia, Siqi Wei, Junyu Wang +8Multimodal Foundation ModelsSpatiotemporal Forecasting

    College of Computer Science and Software Engineering, Shenzhen University · College of Life Sciences and Oceanography, Shenzhen University

  6. Turning TS2Vec into a Foundation Model for Time Series Classification

    Oct 20, 2026Vasilii Feofanov, Zhenghan Tai, Andrei Ermakov +1Time Series ClassificationTime-Series Foundation Models

    NL · University of Toronto · Université de Montréal Mila - Quebec AI Institute

  7. Representing short-term movement to reveal animal identity and learning progress

    Oct 19, 2026Ping Zhang, Quang-Huy Nguyen, Yingtie Lei +5Animal Behavior AnalysisTrajectory Representation Learning

    Boston University · The Ohio State University · University of Chicago

  8. Global Time-Aware Conditioning (GTC): A Mechanism for Time-Aware Pediatric Sleep Representation Learning

    Oct 19, 2026Saurav Raj Pandey, Harlin LeeTemporal Representation LearningSelf-Supervised Time Series Representation Learning

    Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA · School of Data and Information Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

  9. OliO: ODE-based Linear Transition Operator for Self-Supervised Time Series Forecasting

    Oct 16, 2026Kwangryeol Park, Seulki LeeTime Series ForecastingSelf-Supervised Time Series Representation Learning

    Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, Ulsan, South Korea · School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

  10. Local Temporal Contrasts on Human Motion Data Induce a Latent Geometry

    Oct 15, 2026Lourenço A. Rodrigues, Felix PutzeTemporal Representation LearningSelf-Supervised Time Series Representation Learning

    Cognitive Systems Lab FB3, Department of Mathematics and Computer Science, University of Bremen Bremen, 28359 Bremen, Germany

  11. TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series

    Oct 14, 2026Aditya Radhakrishnan, Maxwell A. Xu, Wanting Mao +3Self-Supervised Time Series Representation LearningContrastive Learning

    Health Care Engineering Systems Center, University of Illinois Urbana-Champaign · Google · Department of Computer Science, University of Memphis

  12. IMU-Bench: Benchmarking Wrist IMU Representations Across Kinematic, Semantic and Clinical Tasks

    Oct 14, 2026Yiquan Li, Aidan Bradshaw, Chenfeng Gao +1Wearable SensingMulti-Task Learning

    Northwestern University

  13. W-JEPA: Wavelet Joint-Embedding Predictive Architecture for Wearable Activity Recognition

    Oct 14, 2026Yogesh Prabhu, Loki Natarajan, Jingjing ZouHuman Activity RecognitionRepresentation Learning

    Department of Computer Science and Engineering, University of California San Diego (UCSD). · Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego (UCSD). · UCSD Shiley-Marcos Alzheimer’s Disease Research Center. +1

  14. On Sleep-Wake Disorder Identification from Polysomnographic Representations

    Oct 12, 2026Alvise Dei Rossi, Luigi Fiorillo, Giuliana Monachino +6Self-Supervised Pre-TrainingSelf-Supervised Time Series Representation Learning

    Faculty of Informatics, Università della Svizzera Italiana · MeDiTech, SUPSI · Ente Ospedaliero Cantonale (EOC) +2

  15. Variable Input Masked Autoencoders for Wrist-Worn Accelerometry

    Oct 12, 2026Harish Haresamudram, Wanting Mao, Aditya Radhakrishnan +2Self-Supervised Time Series Representation LearningTime-Series Foundation Models

    University of Illinois Urbana-Champaign · Google

  16. HyBRA-EEG: Hyperbolic Global-Local Brain-Region Aggregation for Self-Supervised EEG Representation Learning

    Oct 12, 2026Xin Liu, Feng Lu, Wei LiElectroencephalographySelf-Supervised Pre-Training

  17. KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting

    Oct 8, 2026Jonas Petersen, Pratik Jawahar, Riccardo Maggioni +6Rare-Event PredictionTime Series Forecasting

    ETH Zurich · Forgis AG · University of Vienna

  18. T-JEPA: A Temporal Joint-Embedding Predictive Architecture for Learning Better Remote Sensing Representations

    Oct 5, 2026Bowen Peng, Li Liu, Yongxiang Liu +3Geospatial Representation LearningSelf-Supervised Time Series Representation Learning

    National University of Defense Technology, Changsha, China

  19. EMG-GPT: Predictive Pretraining on Residual-Quantized EMG Tokens for Hand Pose Estimation

    Oct 4, 2026Ettore Magni, Rolandos Alexandros Potamias, Stefanos Zafeiriou +1Representation LearningSelf-Supervised Time Series Representation Learning

    Imperial College London

  20. STOCK-JEPA: Prior-Anchored Latent Revision Representation Learning in Equity Markets

    Oct 4, 2026Yizhi Luo, Jiahe Yi, Jianhui Zhang +1Quantitative FinanceJoint-Embedding Predictive Architecture

    The Hong Kong University of Science and Technology · Shanghai Jiaotong University · Beijing Zhongguancun Academy

  21. SPERA: Spherical Prior EEG Foundation Model with Geometry- and Frequency-Aware Latent Prediction

    Oct 4, 2026Minsu Kim, Ye-Sung Kim, Hyeseong Jeon +3EEG Foundation ModelsPredictive Representation Learning

    Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea · Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea · Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea

  22. NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings

    Oct 2, 2026Hanrui Lyu, Baiyuan Chen, Tianshu Tan +8Self-Supervised Time Series Representation LearningNeural Encoding

    Northwestern University · University of Cambridge · Johns Hopkins University +3

  23. CORD: Learning Reusable Degradation Representations Across Heterogeneous Physical Systems

    Sep 30, 2026Haibo Li, Zhiguo ZengRepresentation LearningSelf-Supervised Time Series Representation Learning

    CentraleSupélec, Université Paris-Saclay

  24. WinoTS: Wavelet-based Self-Distillation for Time Series Models

    Sep 30, 2026Noam Major, Kathy Razmadze, Yoli ShavitSelf-Supervised Time Series Representation LearningTime Series Data Augmentation

    Bar-Ilan University, Ramat-Gan, Israel

  25. RAE-PPG: Duration-Grounded Retain-and-Extend Pretraining for PPG Foundation Models

    Sep 29, 2026Suyeong Lee, Hochang Lee, Seokyong Sheem +1Self-Supervised Pre-TrainingSelf-Supervised Time Series Representation Learning

    Korea University

  26. Longer Records, Broader Invariance: The Hidden Scaling Problem in Longitudinal Contrastive Learning

    Sep 29, 2026Rameen Mahmood, Xuhai "Orson" Xu, Zachary Beattie +2Contrastive LearningSelf-Supervised Time Series Representation Learning

    New York University · Columbia University · Oregon Health & Science University

  27. Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking

    Sep 28, 2026Guangyu Wang, Jiawei TongSelf-Supervised Time Series Representation LearningSpatiotemporal Forecasting

    Data Science and Artificial Intelligence, Dongbei University of Finance and Economics, Dalian, Liaoning, China · Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, United Kingdom

  28. Phenomenon-Graph JEPA: Label-Efficient Representation Learning for Contactless Cardiorespiratory Sensing

    Sep 26, 2026Constantino Álvarez Casado, Nhi Nguyen, Mohammad Rakibur Rahman +4Self-Supervised Time Series Representation LearningJoint-Embedding Predictive Architecture

    Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland · Candour Ltd, Oulu, Finland

  29. BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment

    Sep 25, 2026Mohammad Nur Hossain Khan, M. S. Krafczyk, Beverly G. Bolster +3Electrocardiogram Representation LearningElectrocardiography

    University of Massachusetts Amherst · University of Illinois Urbana-Champaign

  30. Progressive Memory Transformer: Memory-Aware Attention for Time-Series

    Sep 25, 2026Tord Sture Stangeland, Andreas Köhler, Steffen Mæland +1Self-Supervised Time Series Representation LearningTemporal Representation Learning

    University of Oslo, Department of Informatics, Oslo, Norway · NORSAR, Lillestrøm, Norway · University of Tromsø, Department of Geosciences, Tromsø, Norway +1