mlsanityData to 7 Oct 2026

Domain Adaptation for Time Series Under Feature and Label Shifts

arXiv 2302.03133 ↗First posted 6 Feb 2023cs.LG cs.AIcited by 60

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Abstract (arXiv)

Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents challenges due to the dynamic temporal structure variations across domains. This leads to feature shifts in the time and frequency representations. Additionally, the label distributions of tasks in the source and target domains can differ significantly, posing difficulties in addressing label shifts and recognizing labels unique to the target domain. Effectively transferring complex time series models remains a formidable problem. We present Raincoat, the first model for both closed-set and universal domain adaptation on complex time series. Raincoat addresses feature and label shifts by considering both temporal and frequency features, aligning them across domains, and correcting for misalignments to facilitate the detection of private labels. Additionally, Raincoat improves transferability by identifying label shifts in target domains. Our experiments with 5 datasets and 13 state-of-the-art UDA methods demonstrate that Raincoat can improve transfer learning performance by up to 16.33% and can handle both closed-set and universal domain adaptation.

Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). Builds on 52 of its 90 references that are in the map. First posted on arXiv 6 Feb 2023.

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52 of 90 references.

References · 52

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38 more
  1. 2018Detecting and Correcting for Label Shift with Black Box Predictorscited by 364
  2. 2017Learning Generative Models with Sinkhorn Divergencescited by 310
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  4. 2018Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptationcited by 295
  5. 2017Joint Distribution Optimal Transportation for Domain Adaptationcited by 280
  6. 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
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  15. 2017Few-Shot Adversarial Domain Adaptationcited by 111
  16. 2021Graph-Guided Network for Irregularly Sampled Multivariate Time Seriescited by 110
  17. 2021AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptationcited by 104
  18. 2019Temporal Attentive Alignment for Large-Scale Video Domain Adaptationcited by 100
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  22. 2020Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Datacited by 82
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  25. 2018Optimal Transport for Multi-source Domain Adaptation under Target Shiftcited by 67
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Citing papers per year: 2023: 7; 2024: 18; 2025: 24; 2026: 11.

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  1. 2024Frequency Adaptive Normalization For Non-stationary Time Series Forecastingcited by 45
  2. 2023Label-efficient Time Series Representation Learning: A Reviewcited by 16
  3. 2025Out-of-Distribution Generalization in Time Series: A Surveycited by 15
  4. 2024Continuous Temporal Domain Generalizationcited by 14
  5. 2024Geodesic Optimization for Predictive Shift Adaptation on EEG datacited by 9
  6. 2025Examining and Adapting Time for Multilingual Classification via Mixture of Temporal Expertscited by 9
  7. 2024M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Trainingcited by 8
  8. 2024MixLinear: Extreme Low Resource Multivariate Time Series Forecasting with 0.1K Parameterscited by 8
  9. 2025CATS: Mitigating Correlation Shift for Multivariate Time Series Classificationcited by 7
  10. 2023POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuningcited by 6
  11. 2024An Unsupervised Approach for Periodic Source Detection in Time Seriescited by 5
  12. 2025UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaborationcited by 5
38 more
  1. 2024Phase-driven Domain Generalizable Learning for Nonstationary Time Seriescited by 4
  2. 2024Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptationcited by 4
  3. 2024Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglementcited by 4
  4. 2025Computational Efficiency under Covariate Shift in Kernel Ridge Regressioncited by 4
  5. 2025Cross-Domain Conditional Diffusion Models for Time Series Imputationcited by 4
  6. 2025Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysiscited by 4
  7. 2023Investigating a domain adaptation approach for integrating different measurement instruments in a longitudinal clinical registrycited by 3
  8. 2023Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmarkcited by 3
  9. 2024Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognitioncited by 3
  10. 2024LogoRA: Local-Global Representation Alignment for Robust Time Series Classificationcited by 3
  11. 2025Deep Joint Distribution Optimal Transport for Universal Domain Adaptation on Time Seriescited by 3
  12. 2023Reducing operator complexity in Algebraic Multigrid with Machine Learning Approachescited by 2
  13. 2023Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysiscited by 2
  14. 2024FredNormer: Frequency Domain Normalization for Non-stationary Time Series Forecastingcited by 2
  15. 2024Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantificationcited by 2
  16. 2025Bridging Distribution Gaps in Time Series Foundation Model Pretraining with Prototype-Guided Normalizationcited by 2
  17. 2025Continuous Domain Generalizationcited by 2
  18. 2025PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentationcited by 2
  19. 2025The language of time: a language model perspective on time-series foundation modelscited by 2
  20. 2025Multimodal Causal-Driven Representation Learning for Generalizable Medical Image Segmentationcited by 2
  21. 2024Time and Frequency Synergy for Source-Free Time-Series Domain Adaptationscited by 1
  22. 2025Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptationcited by 1
  23. 2025Time Series Domain Adaptation via Latent Invariant Causal Mechanismcited by 1
  24. 2025Universal Domain Adaptation Benchmark for Time Series Data Representationcited by 1
  25. 2025Mitigating Negative Transfer via Reducing Environmental Disagreementcited by 1
  26. 2025FaultDiffusion: Few-Shot Fault Time Series Generation with Diffusion Modelcited by 1
  27. 2023Phase Matching for Out-of-Distribution Generalizationcited by 0
  28. 2024Zero-shot Microclimate Prediction with Deep Learningcited by 0
  29. 2024Self-Labeling in Multivariate Causality and Quantification for Adaptive Machine Learningcited by 0
  30. 2024Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred Learningcited by 0
  31. 2024A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Seriescited by 0
  32. 2025Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domainscited by 0
  33. 2025Learning Compositional Transferability of Time Series for Source-Free Domain Adaptationcited by 0
  34. 2025From Entanglement to Alignment: Representation Space Decomposition for Unsupervised Time Series Domain Adaptationcited by 0
  35. 2025MoSSDA: A Semi-Supervised Domain Adaptation Framework for Multivariate Time-Series Classification using Momentum Encodercited by 0
  36. 2025Perseus: Interactive Time Series Segmentation with Sparse Supervision via Stateful Memorycited by 0
  37. 2025Resolution-Aware Retrieval Augmented Zero-Shot Forecastingcited by 0
  38. 2026We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series Classificationcited by 0

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