mlsanityData to 7 Oct 2026

Contrastive Learning for Unsupervised Domain Adaptation of Time Series

arXiv 2206.06243 ↗First posted 13 Jun 2022cs.LG cs.AIcited by 36

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Abstract (arXiv)

Unsupervised domain adaptation (UDA) aims at learning a machine learning model using a labeled source domain that performs well on a similar yet different, unlabeled target domain. UDA is important in many applications such as medicine, where it is used to adapt risk scores across different patient cohorts. In this paper, we develop a novel framework for UDA of time series data, called CLUDA. Specifically, we propose a contrastive learning framework to learn contextual representations in multivariate time series, so that these preserve label information for the prediction task. In our framework, we further capture the variation in the contextual representations between source and target domain via a custom nearest-neighbor contrastive learning. To the best of our knowledge, ours is the first framework to learn domain-invariant, contextual representation for UDA of time series data. We evaluate our framework using a wide range of time series datasets to demonstrate its effectiveness and show that it achieves state-of-the-art performance for time series UDA.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). Builds on 52 of its 71 references that are in the map. First posted on arXiv 13 Jun 2022.

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52 of 71 references.

References · 52

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  3. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
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  11. 2019Contrastive Multiview Codingcited by 1,297
  12. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,194
38 more
  1. 2020What Makes for Good Views for Contrastive Learning?cited by 843
  2. 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 733
  3. 2014Deep Domain Confusion: Maximizing for Domain Invariancecited by 597
  4. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 548
  5. 2021TS2Vec: Towards Universal Representation of Time Seriescited by 463
  6. 2019Contrastive Adaptation Network for Unsupervised Domain Adaptationcited by 407
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  8. 2015A Recurrent Latent Variable Model for Sequential Datacited by 362
  9. 2021Time-Series Representation Learning via Temporal and Contextual Contrastingcited by 342
  10. 2021With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representationscited by 324
  11. 2017Multitask learning and benchmarking with clinical time series datacited by 287
  12. 2019Unsupervised Scalable Representation Learning for Multivariate Time Seriescited by 267
  13. 2019Adversarial Domain Adaptation with Domain Mixupcited by 250
  14. 2021Unsupervised Representation Learning for Time Series with Temporal Neighborhood Codingcited by 223
  15. 2018A DIRT-T Approach to Unsupervised Domain Adaptationcited by 215
  16. 2018Multi-Adversarial Domain Adaptationcited by 195
  17. 2016Structured Inference Networks for Nonlinear State Space Modelscited by 182
  18. 2020CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patientscited by 170
  19. 2021Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Datacited by 152
  20. 2021Deep Subdomain Adaptation Network for Image Classificationcited by 119
  21. 2019Temporal Attentive Alignment for Large-Scale Video Domain Adaptationcited by 100
  22. 2022Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusioncited by 96
  23. 2019HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptationcited by 89
  24. 2020Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Datacited by 82
  25. 2020Subject-Aware Contrastive Learning for Biosignalscited by 75
  26. 2021Domain Adaptation for Time Series Forecasting via Attention Sharingcited by 68
  27. 2022Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptationcited by 66
  28. 2021CLDA: Contrastive Learning for Semi-Supervised Domain Adaptationcited by 52
  29. 2019Adversarial Cross-Domain Action Recognition with Co-Attentioncited by 51
  30. 2020Time Series Domain Adaptation via Sparse Associative Structure Alignmentcited by 51
  31. 2021Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixingcited by 49
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  38. 2020Adversarial Bipartite Graph Learning for Video Domain Adaptationcited by 28

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Citing papers per year: 2023: 8; 2024: 14; 2025: 12; 2026: 2.

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  1. 2024Foundation Models for Time Series Analysis: A Tutorial and Surveycited by 234
  2. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  3. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  4. 2023Domain Adaptation for Time Series Under Feature and Label Shiftscited by 60
  5. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  6. 2025Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settingscited by 35
  7. 2023FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Spacecited by 31
  8. 2024Universal Time-Series Representation Learning: A Surveycited by 28
  9. 2023A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directionscited by 22
  10. 2023REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learningcited by 19
  11. 2023Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approachcited by 13
  12. 2024Parametric Augmentation for Time Series Contrastive Learningcited by 12
24 more
  1. 2024Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracingcited by 11
  2. 2025Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Eracited by 11
  3. 2025Deep Learning for Time Series Forecasting: A Surveycited by 10
  4. 2024Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learningcited by 9
  5. 2024M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Trainingcited by 8
  6. 2024DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Seriescited by 7
  7. 2025CATS: Mitigating Correlation Shift for Multivariate Time Series Classificationcited by 7
  8. 2024UniCL: A Universal Contrastive Learning Framework for Large Time Series Modelscited by 5
  9. 2024Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptationcited by 4
  10. 2024Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglementcited by 4
  11. 2025Learning Actionable World Models for Industrial Process Controlcited by 4
  12. 2025Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysiscited by 4
  13. 2024Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shiftcited by 3
  14. 2024LogoRA: Local-Global Representation Alignment for Robust Time Series Classificationcited by 3
  15. 2025Deep Joint Distribution Optimal Transport for Universal Domain Adaptation on Time Seriescited by 3
  16. 2024Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive Learningcited by 2
  17. 2025Robust Emotion Recognition via Bi-Level Self-Supervised Continual Learningcited by 2
  18. 2024Content-aware Balanced Spectrum Encoding in Masked Modeling for Time Series Classificationcited by 1
  19. 2025Time Series Domain Adaptation via Latent Invariant Causal Mechanismcited by 1
  20. 2025LLMs Meet Cross-Modal Time Series Analytics: Overview and Directionscited by 1
  21. 2025From Entanglement to Alignment: Representation Space Decomposition for Unsupervised Time Series Domain Adaptationcited by 0
  22. 2025Uncertainty Awareness on Unsupervised Domain Adaptation for Time Series Datacited by 0
  23. 2026Rethinking Time Series Domain Generalization via Structure-Stratified Calibrationcited by 0
  24. 2026ADAPTive Input Training for Many-to-One Pre-Training on Time-Series Classificationcited by 0