Self-Supervised Time Series Representation Learning

Follow this topic Ask about this topic

Momentum

13 papers in the last four weeks, up 333% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 91

All topics

When Does Self-Supervised Learning Transfer to Time-Series Tasks?
May 19, 2026Noam Major, Kathy Razmadze, Yoli ShavitSelf-Supervised Pre-Training Time Series Classification
Faculty of Engineering, Bar-Ilan University, Israel
KairosHope: A Next-Generation Time-Series Foundation Model for Specialized Classification via Dual-Memory Architecture
May 18, 2026Luis Balderas, José Alberto Rodríguez, Miguel Lastra +2Memory-Augmented Neural Networks Time Series Classification
Department of Computer Science and Artificial Intelligence · DiCITS, iMUDS, DaSCI · Advanced Medical Imaging Group, Instituto de Investigación Biosanitaria de Granada (ibs.Granada) +3
ITGPT: Generative Pretraining on Irregular Timeseries
May 15, 2026Antoine Honoré, Ming XiaoIrregular Time-Series Modeling Self-Supervised Time Series Representation Learning
⋆Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
ChronoEarth-492K: A Large Scale and Long Horizon Spatiotemporal Hyperspectral Earth Observation Dataset and Benchmark
May 15, 2026Haozhe Si, Yuxuan Wan, Yuqing Wang +2Geospatial Foundation Models Self-Supervised Time Series Representation Learning
Department of Electrical and Computer Engineering · Siebel School of Computing and Data Science University of Illinois Urbana-Champaign, IL, USA
CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervision
May 13, 2026Xuanhao Luo, Zhizhen Li, Yuchen LiuSelf-Supervised Pre-Training Self-Supervised Time Series Representation Learning
North Carolina State University, USA
ECG-NAT: A Self-supervised Neighborhood Attention Transformer for Multi-lead Electrocardiogram Classification
May 13, 2026Mahsa Gazeran, Sayvan Soleymanbaigi, Fatemeh Daneshfar +2Self-Supervised Time Series Representation Learning Transformer Attention
Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran · Department of Mathematics and Operational Research, University of Mons, Mons, Belgium
VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals
May 13, 2026Yuxuan Weng, Wenhan Luo, Qijia ShaoMissing-Modality Learning Disentangled Representation Learning
The Hong Kong University of Science and Technology (HKUST)
Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study
May 12, 2026M A Al-Masud, Nils StrodthoffElectrocardiography Self-Supervised Time Series Representation Learning
AI4Health Division Carl von Ossietzky Universität Oldenburg Oldenburg, Lower Saxony, Germany
Martingale-Consistent Self-Supervised Learning
May 12, 2026Moritz Gögl, Hanwen Xing, Christopher YauSelf-Supervised Learning Self-Supervised Time Series Representation Learning
University of Oxford · Health Data Research UK
HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series
May 11, 2026Jonas Petersen, Gian-Alessandro Lombardi, Riccardo Maggioni +3Event Time Prediction Self-Supervised Time Series Representation Learning
1ETH Zurich · 2Forgis · University of Vienna
WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms
May 9, 2026Peng Cao, Zhijian Yang, Tennison Liu +17Self-Supervised Time Series Representation Learning Hierarchical Representation Learning
Google Research · University of Washington · MIT
Event Fields: Learning Latent Event Structure for Waveform Foundation Models
May 9, 2026Li Na, Yuanyun Zhang, Shi LiIrregular Time-Series Modeling Latent Dynamics Modeling
University of Science and Technology of China · Columbia University
BenchHAR: Benchmarking Self-Supervised Learning for Generalizable Sensor-based Activity Recognition
May 8, 2026Yize Cai, Rui Feng, Anlan Yu +2Human Activity Recognition Self-Supervised Time Series Representation Learning
The Hong Kong University of Science and Technology (Guangzhou) · Peking University · Singapore-MIT Alliance for Research and Technology
From Muscle Bursts to Motor Intent: Self-Supervised Token Modeling for Heterogeneous EMG
May 5, 2026Zhenghao Huang, Huilin Yao, Kaikai WangEMG-Based Gesture Recognition Human Activity Recognition
South China University of Technology
Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks
May 4, 2026William Lehn-Schiøler, Magnus Ruud Kjær, Phillip Hempel +7Self-Supervised Time Series Representation Learning Cross-Domain Transfer Learning
BrainCapture ApS, Kongens Lyngby, Denmark · DTU Health Tech, Technical University of Denmark, Kongens Lyngby, Denmark · DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark +4
Rhamba: Region-Aware Hybrid Attention-Mamba Framework for Self-Supervised Learning in Resting-State fMRI
May 2, 2026Ruthwik Reddy Doodipala, Pankaj Pandey, Pratheek Eranki +3Self-Supervised Pre-Training Self-Supervised Time Series Representation Learning
1St. Jude Children’s Research Hospital · 2The University of Memphis
CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
May 1, 2026Hada Melino Muhammad, Zechen Li, Flora Salim +1Cross-Modal Representation Learning Self-Supervised Time Series Representation Learning
University of New South Wales · Google Research
Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization
Apr 30, 2026Huayu Li, ZhengXiao He, Xiwen Chen +4Disentangled Representation Learning Rate-Distortion Theory
University of Arizona, AZ, USA · Morgan Stanley, NY, USA · Clemson University, SC, USA +2
UniBCI: Towards a Unified Pretrained Model for Invasive Brain-Computer Interfaces
Apr 30, 2026Binjie Hong, Rui Xiong, Liyuan Han +1Brain-Computer Interfaces Self-Supervised Pre-Training
Brain Science Data Center · CEBSIT, Institute of Neuroscience
Deep Clustering for Climate: Analyzing Teleconnections through Learned Categorical States
Apr 24, 2026Lívia Meinhardt, Dário OliveiraRepresentation Learning Time Series Classification
FGV EMAp
Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs
Apr 24, 2026Jose Geraldo Fernandes, Luiz Facury, Pedro Robles Dutenhefner +1Latent Dynamics Modeling Healthcare
Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte, Brazil
Do Masked Autoencoders Improve Downhole Prediction? An Empirical Study on Real Well Drilling Data
Apr 21, 2026Aleksander Berezowski, Hassan Hassanzadeh, Gouri GindeMasked Autoencoders Self-Supervised Time Series Representation Learning
Schulich School of Engineering Department of Electrical and Software Engineering University of Calgary Calgary, Canada · Schulich School of Engineering Department of Chemical and Petroleum Engineering University of Calgary Calgary, Canada
Disentangling Damage from Operational Variability: A Label-Free Self-Supervised Representation Learning Framework for Output-Only Structural Damage Identification
Apr 21, 2026Xudong Jian, Charikleia Stoura, Simon Scandella +1Disentangled Representation Learning Self-Supervised Time Series Representation Learning
Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zürich, Switzerland. · Department of Mechanical Engineering, Politecnico di Milano, Italy
Parkinson's Disease Detection via Self-Supervised Dual-Channel Cross-Attention on Bilateral Wrist-Worn IMU Signals
Apr 20, 2026Meheru ZannatCross-Attention Self-Supervised Time Series Representation Learning
Khulna University of Engineering & Technology (KUET), Bangladesh
PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
Feb 1, 2026Jinju Park, Seokho KangSelf-Supervised Time Series Representation Learning Time Series Representation Learning
Department of Industrial Engineering, Sungkyunkwan University
SingLEM: Single-Channel Large EEG Model
Sep 22, 2025Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue +5Representation Learning Self-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
EEGDM: Learning EEG Representation with Latent Diffusion Model
Aug 28, 2025Shaocong Wang, Tong Liu, Yihan Li +6Electroencephalography Latent Diffusion Models
Tsinghua University · Tianjin University
EEGDM: Label-Efficient EEG Representation Learning with Generative Diffusion Model
Aug 13, 2025Jia Hong Puah, Sim Kuan Goh, Ziwei Zhang +6Electroencephalography EEG 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
FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation
Jul 22, 2025Pingyi Fan, Anbai Jiang, Shuwei Zhang +9Representation Learning Self-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
Mantis: Lightweight Foundation Model for Time Series Classification
Feb 21, 2025Vasilii Feofanov, Songkang Wen, Shifeng Xie +10Time Series Classification Self-Supervised Time Series Representation Learning
1. Huawei Noah’s Ark Lab · 3. Paris Cit´e University · 4. Helmholtz Munich +3

May 19, 2026, cs.LGJ/K move · Enter open · S save

When Does Self-Supervised Learning Transfer to Time-Series Tasks?

Noam Major, Kathy Razmadze, Yoli Shavit

Faculty of Engineering, Bar-Ilan University, Israel

Self-Supervised Pre-Training Time Series Classification Self-Supervised Time Series Representation Learning Time Series Forecasting Transfer Learning

Open paper AskPDF arXiv

Self-supervised learning (SSL) assumes that solving pretext tasks on unlabeled data yields representations that transfer effectively across downstream applications via linear probing or fine-tuning. While this paradigm has driven major progress in vision and language, its benefits for time series remain under-investigated and often confounded by inconsistent experimental controls. To address this gap, we benchmark seven representative methods from five key SSL paradigms across anomaly detection, classification, and forecasting under parameter- and data-matched budgets. We find that transfer efficacy depends heavily on the downstream task. SSL yields substantial gains in anomaly detection and provides effective initializations for classification under fine-tuning, but offers limited to no advantage over non-pre-trained controls in forecasting. Furthermore, linear probing does not reliably predict fine-tuning performance, synthetic pre-training is often competitive with real-world corpora, and scaling encoder depth degrades forecasting accuracy. We synthesize these empirical results into practical evaluation and development guidelines for time-series SSL.

Momentum

Related topics

Latest papers 91