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https://github.com/itouchz/awesome-deep-time-series-representations
A curated list of state-of-the-art papers on deep learning for universal representations of time series.
https://github.com/itouchz/awesome-deep-time-series-representations
List: awesome-deep-time-series-representations
deep-learning representation-learning survey-paper time-series time-series-analysis
Last synced: 16 days ago
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A curated list of state-of-the-art papers on deep learning for universal representations of time series.
- Host: GitHub
- URL: https://github.com/itouchz/awesome-deep-time-series-representations
- Owner: itouchz
- License: mit
- Created: 2022-03-10T11:54:04.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-05-13T11:33:04.000Z (7 months ago)
- Last Synced: 2024-05-19T14:08:21.384Z (7 months ago)
- Topics: deep-learning, representation-learning, survey-paper, time-series, time-series-analysis
- Homepage:
- Size: 2.01 MB
- Stars: 84
- Watchers: 5
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-deep-time-series-representations - A curated list of state-of-the-art papers on deep learning for universal representations of time series. (Other Lists / Monkey C Lists)
README
# Awesome Deep Time-Series Representations
This is a repository to help all readers who are interested in learning universal representations of time series with deep learning. If your papers are missing or you have other requests, please post an issue, create a pull request, or contact [email protected]_.
We will update this repository at a regular basis in accordance with the top-tier conference publication cycles to maintain up-to-date.
> Next Batch: ICDM 2024, CIKM 2024, NeurIPS 2024
**Accompanying Paper**: [Universal Time-Series Representation Learning: A Survey](https://arxiv.org/abs/2401.03717)
```bibtex
@article{trirat2024universal,
title={Universal Time-Series Representation Learning: A Survey},
author={Patara Trirat and Yooju Shin and Junhyeok Kang and Youngeun Nam and Jihye Na and Minyoung Bae and Joeun Kim and Byunghyun Kim and Jae-Gil Lee},
journal={arXiv preprint arXiv:2401.03717},
year={2024}
}
```
## Proposed Taxonomy
## Contents
- [Related Survey Papers](https://github.com/itouchz/awesome-deep-time-series-representations/tree/main#related-surveys)
- [Time-Series Data Mining and Analysis](https://github.com/itouchz/awesome-deep-time-series-representations/tree/main#time-series-data-mining-and-analysis)
- [General Representation Learning](https://github.com/itouchz/awesome-deep-time-series-representations/tree/main#representation-learning)
- [Research Papers](https://github.com/itouchz/awesome-deep-time-series-representations/tree/main#research-papers)
- [Neural Architectural Approaches](https://github.com/itouchz/awesome-deep-time-series-representations/tree/main#neural-architectural-approaches)
- [Learning-Focused Approaches](https://github.com/itouchz/awesome-deep-time-series-representations/tree/main#learning-focused-approaches)
- [Data-Centric Approaches](https://github.com/itouchz/awesome-deep-time-series-representations/tree/main#data-centric-approaches)
- [Other Repositories](https://github.com/itouchz/awesome-deep-time-series-representations/tree/main#neighbor-repositories)
## Related Surveys (Latest Update: July, 2024)
### Time-Series Data Mining and Analysis
| **Title** | **Affiliation** | **Venue** | **Year** |
| --------------------------------------------------------------------------------------------------- | ------------------------------------------------- | ----------------------------------- | -------- |
| [Discrete Wavelet Transform-based Time Series Analysis and Mining](https://dl.acm.org/doi/abs/10.1145/1883612.1883613) | University of Maryland | ACM CSUR | 2011 |
| [Time-Series Data Mining](https://dl.acm.org/doi/10.1145/2379776.2379788) | IRCAM | ACM CSUR | 2012 |
| [A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling](https://www.sciencedirect.com/science/article/pii/S0167865514000221) | Örebro University | Pattern Recognition Letters | 2014 |
| [Time-series clustering – A decade review](https://www.sciencedirect.com/science/article/pii/S0306437915000733) | University of Malaya | Information Systems | 2015 |
| [Deep Learning for Time-Series Analysis](https://arxiv.org/abs/1701.01887) | University of Kaiserslautern | arXiv | 2017 |
| [A survey of methods for time series change point detection](https://link.springer.com/article/10.1007/s10115-016-0987-z) | Washington State University | KAIS | 2017 |
| [Survey on time series motif discovery](https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1199) | Ostwestfalen-Lippe University of Applied Sciences | WIDM | 2017 |
| [Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review](https://www.mdpi.com/2076-3417/9/7/1345) | Ecole Nationale des Sciences de l’Informatique | MDPI Applied Sciences | 2019 |
| [Deep learning for time series classification: a review](https://link.springer.com/article/10.1007/s10618-019-00619-1) | Université Haute Alsace | Data Mining and Knowledge Discovery | 2019 |
| [Anomaly Detection for IoT Time-Series Data: A Survey](https://ieeexplore.ieee.org/abstract/document/8926446) | University of Keele | IEEE IoT-J | 2019 |
| [A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data](https://arxiv.org/abs/2010.12493) | Peking University | arXiv | 2020 |
| [Approaches and Applications of Early Classification of Time Series: A Review](https://ieeexplore.ieee.org/abstract/document/9207873/) | Indian Institute of Technology (BHU) Varanasi | IEEE TAI | 2020 |
| [A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series](https://arxiv.org/abs/2012.00168) | University of Massachusetts Amherst | NeurIPS Workshop on ML-RSA | 2020 |
| [A Review of Deep Learning Models for Time Series Prediction](https://ieeexplore.ieee.org/document/8742529) | Dalian University of Technology | IEEE Sensors Journal | 2021 |
| [An empirical survey of data augmentation for time series classification with neural networks](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254841) | Kyushu University | PLOS ONE | 2021 |
| [Time-series forecasting with deep learning: a survey](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2020.0209) | University of Oxford | Phil.Trans.R.Soc.A | 2021 |
| [A Review on Outlier/Anomaly Detection in Time Series Data](https://dl.acm.org/doi/abs/10.1145/3444690) | Basque Research and Technology Alliance | ACM CSUR | 2021 |
| [A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives](https://link.springer.com/chapter/10.1007/978-3-030-73100-7_60) | University of Newcastle | FICC | 2021 |
| [Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines](https://ieeexplore.ieee.org/abstract/document/9523565) | Seoul National University | IEEE Access | 2021 |
| [Time Series Data Augmentation for Deep Learning: A Survey](https://www.ijcai.org/proceedings/2021/0631.pdf) | Alibaba Group | IJCAI | 2021 |
| [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://www.worldscientific.com/doi/abs/10.1142/S0129065721300011) | University of Sevilla | IJNS | 2021 |
| [Experimental Comparison and Survey of Twelve Time Series Anomaly Detection Algorithms](https://jair.org/index.php/jair/article/view/12698) | Verint | JAIR | 2021 |
| [Causal inference for time series analysis: problems, methods and evaluation](https://link.springer.com/article/10.1007/s10115-021-01621-0) | Arizona State University | KAIS | 2021 |
| [End-to-end deep representation learning for time series clustering: a comparative study](https://link.springer.com/article/10.1007/s10618-021-00796-y) | Université de Haute Alsace | Data Mining and Knowledge Discovery | 2022 |
| [Survey and Evaluation of Causal Discovery Methods for Time Series](https://www.jair.org/index.php/jair/article/view/13428) | Université Grenoble Alpes | JAIR | 2022 |
| [A Review of Recurrent Neural Network-Based Methods in Computational Physiology](https://ieeexplore.ieee.org/abstract/document/9705533) | University of Pittsburgh | IEEE TNNLS | 2022 |
| [Deep Learning for Time Series Anomaly Detection: A Survey](https://arxiv.org/abs/2211.05244) | Monash University | arXiv | 2022 |
| [Deep Learning for Time Series Forecasting: Tutorial and Literature Survey](https://dl.acm.org/doi/full/10.1145/3533382) | Amazon Research | ACM CSUR | 2022 |
| [Transformers in Time Series: A Survey](https://arxiv.org/abs/2202.07125) | Alibaba Group | IJCAI | 2023 |
| [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://arxiv.org/abs/2302.02515) | Monash University | arXiv | 2023 |
| [Label-efficient Time Series Representation Learning: A Review](https://arxiv.org/abs/2302.06433) | Nanyang Technological University | arXiv | 2023 |
| [Neural Time Series Analysis with Fourier Transform: A Survey](https://arxiv.org/abs/2302.02173) | Beijing Institute of Technology | arXiv | 2023 |
| [A Survey on Dimensionality Reduction Techniques for Time-Series Data](https://ieeexplore.ieee.org/document/10107391) | University of Colorado Boulder | IEEE Access | 2023 |
| [Long sequence time-series forecasting with deep learning: A survey](https://www.sciencedirect.com/science/article/pii/S1566253523001355) | Southwest Jiaotong University | Information Fusion | 2023 |
| [Data Augmentation techniques in time series domain: a survey and taxonomy](https://link.springer.com/article/10.1007/s00521-023-08459-3) | Universidad Politécnica de Madrid | Neural Computing & Applications | 2023 |
| [Diffusion Models for Time Series Applications: A Survey](https://arxiv.org/abs/2305.00624) | University of Sydney | arXiv | 2023 |
| [A Survey on Time-Series Pre-Trained Models](https://arxiv.org/abs/2305.10716) | South China University of Technology | arXiv | 2023 |
| [Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review](https://www.mdpi.com/1424-8220/23/9/4221) | RMIT University | MDPI Sensors | 2023 |
| [Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects](https://arxiv.org/abs/2306.10125) | Zhejiang University | IEEE TPAMI | 2024 |
| [Unsupervised Representation Learning for Time Series: A Review](https://arxiv.org/abs/2308.01578) | Shandong University | arXiv | 2023 |
| [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://arxiv.org/abs/2310.10196) | Monash University | arXiv | 2023 |
| [Foundation Models for Time Series Analysis: A Tutorial and Survey](https://arxiv.org/pdf/2403.14735.pdf) | The Hong Kong University of Science and Technology | arXiv | 2024 |
| [Large Language Models for Time Series: A Survey](https://arxiv.org/pdf/2402.01801.pdf) | University of California, San Diego | arxiv | 2024 |
| [Empowering Time Series Analysis with Large Language Models: A Survey](https://arxiv.org/pdf/2402.03182.pdf) | University of Connecticut | arxiv | 2024 |
| [A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model](https://arxiv.org/abs/2405.02358) | Hong Kong University of Science and Technology | arxiv | 2024 |
| [Position: What Can Large Language Models Tell Us about Time Series Analysis](https://openreview.net/forum?id=iroZNDxFJZ) | Griffith University, Chinese Academy of Sciences, The Hong Kong University of Science and Technology (Guangzhou) | ICML | 2024 |
| [Deep Time Series Models: A Comprehensive Survey and Benchmark](https://arxiv.org/abs/2407.13278) | Tsinghua University | arXiv | 2024 |
### Representation Learning
| **Title** | **Affiliation** | **Venue** | **Year** |
| ---------------------------------------------------------------------------------------------------------------------- | --------------------------------- | ------------------------------- | -------- |
| [Representation Learning: A Review and New Perspectives](https://ieeexplore.ieee.org/document/6472238) | University of Montreal | IEEE TPAMI | 2013 |
| [A Survey of Multi-View Representation Learning](https://ieeexplore.ieee.org/abstract/document/8471216) | Zhejiang University | IEEE TKDE | 2019 |
| [Deep Multimodal Representation Learning: A Survey](https://ieeexplore.ieee.org/abstract/document/8715409/) | Fuzhou University | IEEE Access | 2019 |
| [A Survey on Representation Learning for User Modeling](https://www.ijcai.org/proceedings/2020/0695.pdf) | University of Georgia | IJCAI | 2020 |
| [A survey on deep geometry learning: From a representation perspective](https://link.springer.com/article/10.1007/s41095-020-0174-8) | Chinese Academy of Sciences | Computational Visual Media | 2020 |
| [A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications](https://ieeexplore.ieee.org/document/9043500/) | Xiamen University | IEEE Access | 2020 |
| [Contrastive Representation Learning: A Framework and Review](https://ieeexplore.ieee.org/abstract/document/9226466) | Dublin City University | IEEE Access | 2020 |
| [Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data](https://arxiv.org/abs/2206.02353) | RMIT University | arXiv | 2022 |
| [Self-Supervised Representation Learning: Introduction, advances, and challenges](https://ieeexplore.ieee.org/abstract/document/9770283) | University of Edinburgh | IEEE Signal Processing Magazine | 2022 |
| [A Brief Overview of Universal Sentence Representation Methods: A Linguistic View](https://dl.acm.org/doi/10.1145/3482853) | KU Leuven | ACM CSUR | 2022 |
| [Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding](https://dl.acm.org/doi/full/10.1145/3491206) | Soochow University | ACM CSUR | 2022 |
| [Evaluation Methods for Representation Learning: A Survey](https://www.ijcai.org/proceedings/2022/0776.pdf) | University of Tokyo | IJCAI | 2022 |
| [Self-Supervised Speech Representation Learning: A Review](https://ieeexplore.ieee.org/abstract/document/9893562) | Meta | IEEE JSTSP | 2022 |
| [A Survey on Hypergraph Representation Learning](https://dl.acm.org/doi/abs/10.1145/3605776) | Università degli Studi di Torino | ACM CSUR | 2023 |
| [Representation learning for knowledge fusion and reasoning in Cyber–Physical–Social Systems: Survey and perspectives](https://www.sciencedirect.com/science/article/pii/S156625352200135X) | Hainan University | Information Fusion | 2023 |
| [Survey of Deep Representation Learning for Speech Emotion Recognition](https://ieeexplore.ieee.org/abstract/document/9543566) | University of Southern Queensland | IEEE TAFFC | 2023 |
| [Graph Representation Learning and Its Applications: A Survey](https://www.mdpi.com/1424-8220/23/8/4168) | Catholic University of Korea | MDPI Sensors | 2023 |
| [Graph Representation Learning Meets Computer Vision: A Survey](https://ieeexplore.ieee.org/abstract/document/9844822) | Xidian University | IEEE TAI | 2023 |
| [A Comprehensive Survey on Deep Graph Representation Learning](https://arxiv.org/abs/2304.05055) | Peking University | arXiv | 2023 |
| [Dynamic Graph Representation Learning with Neural Networks: A Survey](https://arxiv.org/abs/2304.05729) | University of Rouen Normandy | arXiv | 2023 |
| [Multiscale Representation Learning for Image Classification: A Survey](https://ieeexplore.ieee.org/abstract/document/9650759) | Xidian University | IEEE TAI | 2023 |
| [A Survey on Protein Representation Learning: Retrospect and Prospect](https://arxiv.org/abs/2301.00813) | Westlake University | arXiv | 2023 |
## Research Papers (Latest Update: KDD 2024)
### Neural Architectural Approaches
> Studies in this group focus on the novel design of neural architectures by combining basic building blocks or redesigning a neural architecture from scratch to improve the capability of capturing temporal dependencies and inter-relationships between variables of multivariate time series. We can further categorize the studies into the basic block combination and innovative redesign categories based on the degree of architecture adjustment.
| Year | Title | Venue |
| ---- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------- |
| 2018 | [](https://www.sciencedirect.com/science/article/abs/pii/S0031320319302766)[Learning representations of multivariate time series with missing data](https://www.sciencedirect.com/science/article/abs/pii/S0031320319302766) | Pattern Recognition |
| 2018 | [](https://dl.acm.org/doi/abs/10.1145/3219819.3220060)[Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis](https://dl.acm.org/doi/abs/10.1145/3219819.3220060) | KDD |
| 2019 | [](https://papers.nips.cc/paper_files/paper/2019/hash/42a6845a557bef704ad8ac9cb4461d43-Abstract.html)[Latent ODEs for Irregularly-Sampled Time Series](https://papers.nips.cc/paper_files/paper/2019/hash/42a6845a557bef704ad8ac9cb4461d43-Abstract.html) | NeurIPS |
| 2019 | [](https://link.springer.com/chapter/10.1007/978-3-030-46133-1_19)[Learning Disentangled Representations of Satellite Image Time Series](https://link.springer.com/chapter/10.1007/978-3-030-46133-1_19) | ECML PKDD |
| 2019 | [](https://proceedings.neurips.cc/paper_files/paper/2019/hash/53c6de78244e9f528eb3e1cda69699bb-Abstract.html)[Unsupervised Scalable Representation Learning for Multivariate Time Series](https://proceedings.neurips.cc/paper_files/paper/2019/hash/53c6de78244e9f528eb3e1cda69699bb-Abstract.html) | NeurIPS |
| 2019 | [](https://ieeexplore.ieee.org/document/8736337)[Audio Word2vec: Sequence-to-Sequence Autoencoding for Unsupervised Learning of Audio Segmentation and Representation](https://ieeexplore.ieee.org/document/8736337) | IEEE/ACM TASLP |
| 2019 | [](https://dl.acm.org/doi/10.1145/3357384.3358155)[Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks](https://dl.acm.org/doi/10.1145/3357384.3358155) | CIKM |
| 2020 | [](https://ieeexplore.ieee.org/iel7/76/4358651/09051710.pdf)[A real-time action representation with temporal encoding and deep compression](https://ieeexplore.ieee.org/iel7/76/4358651/09051710.pdf) | IEEE TCSVT |
| 2020 | [](https://ieeexplore.ieee.org/document/8685795)[End-to-End Incomplete Time-Series Modeling From Linear Memory of Latent Variables](https://ieeexplore.ieee.org/document/8685795) | IEEE TCYB |
| 2020 | [](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19)[Memory-Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
| 2020 | [](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10)[Temporal Aggregate Representations for Long-Range Video Understanding](https://link.springer.com/chapter/10.1007/978-3-030-58517-4_10) | ECCV |
| 2021 | [](https://ieeexplore.ieee.org/abstract/document/9679144)[Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting](https://ieeexplore.ieee.org/abstract/document/9679144) | ICDM |
| 2021 | [](https://www.ijcai.org/proceedings/2021/414)[TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data](https://www.ijcai.org/proceedings/2021/414) | IJCAI |
| 2021 | [](https://openaccess.thecvf.com/content/CVPR2021/html/Guo_SSAN_Separable_Self-Attention_Network_for_Video_Representation_Learning_CVPR_2021_paper.html)[SSAN: Separable Self-Attention Network for Video Representation Learning](https://openaccess.thecvf.com/content/CVPR2021/html/Guo_SSAN_Separable_Self-Attention_Network_for_Video_Representation_Learning_CVPR_2021_paper.html) | CVPR |
| 2021 | [](https://openreview.net/forum?id=4c0J6lwQ4_)[Multi-Time Attention Networks for Irregularly Sampled Time Series](https://openreview.net/forum?id=4c0J6lwQ4_) | ICLR |
| 2021 | [](https://ojs.aaai.org/index.php/AAAI/article/view/16846)[Time Series Domain Adaptation via Sparse Associative Structure Alignment](https://ojs.aaai.org/index.php/AAAI/article/view/16846) | AAAI |
| 2021 | [](https://www.sciencedirect.com/science/article/pii/S0020025520312287)[A deep multi-task representation learning method for time series classification and retrieval](https://www.sciencedirect.com/science/article/pii/S0020025520312287) | Information Sciences |
| 2021 | [](https://dl.acm.org/doi/10.1145/3447548.3467401)[A Transformer-based Framework for Multivariate Time Series Representation Learning](https://dl.acm.org/doi/10.1145/3447548.3467401) | KDD |
| 2021 | [](https://www.sciencedirect.com/science/article/pii/S0950705120306808)[DeLTa: Deep local pattern representation for time-series clustering and classification using visual perception](https://www.sciencedirect.com/science/article/pii/S0950705120306808) | KBS |
| 2021 | [](https://proceedings.neurips.cc/paper_files/paper/2021/hash/51200d29d1fc15f5a71c1dab4bb54f7c-Abstract.html)[TriBERT: Human-centric Audio-visual Representation Learning](https://proceedings.neurips.cc/paper_files/paper/2021/hash/51200d29d1fc15f5a71c1dab4bb54f7c-Abstract.html) | NeurIPS |
| 2022 | [](https://arxiv.org/abs/2212.03560)[CrossPyramid: Neural Ordinary Differential Equations Architecture for Partially-observed Time-series](https://arxiv.org/abs/2212.03560) | arXiv |
| 2022 | [](https://dl.acm.org/doi/abs/10.1145/3485447.3512030)[EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting](https://dl.acm.org/doi/abs/10.1145/3485447.3512030) | WebConf |
| 2022 | [](https://proceedings.mlr.press/v162/schirmer22a.html)[Modeling Irregular Time Series with Continuous Recurrent Units](https://proceedings.mlr.press/v162/schirmer22a.html) | ICML |
| 2022 | [](https://dl.acm.org/doi/abs/10.1145/3534678.3539140)[Towards Learning Disentangled Representations for Time Series](https://dl.acm.org/doi/abs/10.1145/3534678.3539140) | KDD |
| 2022 | [](https://dl.acm.org/doi/abs/10.1145/3511808.3557386)[MARINA: An MLP-Attention Model for Multivariate Time-Series Analysis](https://dl.acm.org/doi/abs/10.1145/3511808.3557386) | CIKM |
| 2022 | [](https://dl.acm.org/doi/10.1145/3534678.3539329)[TARNet : Task-Aware Reconstruction for Time-Series Transformer](https://dl.acm.org/doi/10.1145/3534678.3539329) | KDD |
| 2022 | [](https://ieeexplore.ieee.org/document/9878692/)[Weakly Paired Associative Learning for Sound and Image Representations via Bimodal Associative Memory](https://ieeexplore.ieee.org/document/9878692/) | CVPR |
| 2022 | [](https://proceedings.mlr.press/v151/tonekaboni22a.html)[Decoupling Local and Global Representations of Time Series](https://proceedings.mlr.press/v151/tonekaboni22a.html) | AISTATS |
| 2022 | [](https://openreview.net/forum?id=DZ2FaoMhWRb)[HyperTime: Implicit Neural Representations for Time Series](https://openreview.net/forum?id=DZ2FaoMhWRb) | NeurIPS (Workshop) |
| 2022 | [](https://ieeexplore.ieee.org/abstract/document/9713748)[TCGL: Temporal Contrastive Graph for Self-Supervised Video Representation Learning](https://ieeexplore.ieee.org/abstract/document/9713748) | IEEE TIP |
| 2022 | [](https://proceedings.mlr.press/v162/yang22e.html)[Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion](https://proceedings.mlr.press/v162/yang22e.html) | ICML |
| 2023 | [](https://openreview.net/forum?id=YJDz4F2AZu)[ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling](https://openreview.net/forum?id=YJDz4F2AZu) | NeurIPS |
| 2023 | [](https://dl.acm.org/doi/abs/10.1145/3583780.3615097)[TriD-MAE: A Generic Pre-trained Model for Multivariate Time Series with Missing Values](https://dl.acm.org/doi/abs/10.1145/3583780.3615097) | CIKM |
| 2023 | [](https://proceedings.mlr.press/v202/ansari23a.html)[Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series](https://proceedings.mlr.press/v202/ansari23a.html) | ICML |
| 2023 | [](https://openreview.net/forum?id=sOQBHlCmzp)[Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series](https://openreview.net/forum?id=sOQBHlCmzp) | NeurIPS |
| 2023 | [](https://arxiv.org/pdf/2311.14782)[One Fits All: Universal Time Series Analysis by Pretrained LM and Specially Designed Adaptors](https://arxiv.org/pdf/2311.14782) | arXiv |
| 2023 | [](https://ieeexplore.ieee.org/document/10036065/)[Multivariate Time Series Representation Learning via Hierarchical Correlation Pooling Boosted Graph Neural Network](https://ieeexplore.ieee.org/document/10036065/) | IEEE TAI |
| 2023 | [](https://openreview.net/forum?id=gMS6FVZvmF)[One Fits All: Power General Time Series Analysis by Pretrained LM](https://openreview.net/forum?id=gMS6FVZvmF) | NeurIPS |
| 2023 | [](https://arxiv.org/abs/2302.06375)[One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data](https://arxiv.org/abs/2302.06375) | arXiv |
| 2023 | [](https://arxiv.org/abs/2306.05880)[Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations](https://arxiv.org/abs/2306.05880) | arXiv |
| 2023 | [](https://arxiv.org/abs/2303.13804)[UniTS: A Universal Time Series Analysis Framework with Self-supervised Representation Learning](https://arxiv.org/abs/2303.13804) | arXiv |
| 2023 | [](https://openreview.net/forum?id=OUWckW2g3j)[Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion](https://openreview.net/forum?id=OUWckW2g3j) | ICML |
| 2023 | [](https://openreview.net/forum?id=ju_Uqw384Oq)[TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://openreview.net/forum?id=ju_Uqw384Oq) | ICLR |
| 2023 | [](https://www.vldb.org/pvldb/vol17/p386-wang.pdf)[A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning](https://www.vldb.org/pvldb/vol17/p386-wang.pdf) | VLDB |
| 2023 | [](https://arxiv.org/abs/2310.11959)[A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis](https://arxiv.org/abs/2310.11959) | arXiv |
| 2023 | [](https://openreview.net/forum?id=2EpjkjzdCAa)[Effectively Modeling Time Series with Simple Discrete State Spaces](https://openreview.net/forum?id=2EpjkjzdCAa) | ICLR |
| 2023 | [](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr)[FEAT: A general framework for Feature-aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
| 2023 | [](https://arxiv.org/abs/2306.06579)[Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach](https://arxiv.org/abs/2306.06579) | arXiv |
| 2023 | [](https://arxiv.org/abs/2303.01034)[Multi-Task Self-Supervised Time-Series Representation Learning](https://arxiv.org/abs/2303.01034) | arXiv |
| 2023 | [](https://dl.acm.org/doi/10.1145/3580305.3599549)[WHEN: A Wavelet-DTW Hybrid Attention Network for Heterogeneous Time Series Analysis](https://dl.acm.org/doi/10.1145/3580305.3599549) | KDD |
| 2023 | [](https://dl.acm.org/doi/10.1145/3580305.3599508)[Sparse Binary Transformers for Multivariate Time Series Modeling](https://dl.acm.org/doi/10.1145/3580305.3599508) | KDD |
| 2024 | [](https://openreview.net/forum?id=QVVSb0GMXK)[NEWTIME: NUMERICALLY MULTI-SCALED EMBEDDING FOR LARGE-SCALE TIME SERIES PRETRAINING](https://openreview.net/forum?id=QVVSb0GMXK) | arXiv |
| 2024 | [](https://arxiv.org/abs/2309.05305)[Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data](https://arxiv.org/abs/2309.05305) | AAAI |
| 2024 | [](https://openreview.net/forum?id=bWcnvZ3qMb)[FITS: MODELING TIME SERIES WITH 10k PARAMETERS](https://openreview.net/forum?id=bWcnvZ3qMb) | ICLR |
| 2024 | [](https://openreview.net/pdf?id=c56TWtYp0W)[GAFORMER: ENHANCING TIMESERIES TRANSFORMERS THROUGH GROUP-AWARE EMBEDDINGS](https://openreview.net/pdf?id=c56TWtYp0W) | ICLR |
| 2024 | [](https://openreview.net/forum?id=3y2TfP966N)[T-REP: REPRESENTATION LEARNING FOR TIME SERIES USING TIME-EMBEDDINGS](https://openreview.net/forum?id=3y2TfP966N) | ICLR |
| 2024 | [](https://arxiv.org/abs/2403.00131)[UNITS: A Unified Multi-Task Time Series Model](https://arxiv.org/abs/2403.00131) | arXiv |
| 2024 | [](https://openreview.net/forum?id=4VIgNuQ1pY)[STABLE NEURAL STOCHASTIC DIFFERENTIAL EQUATIONS IN ANALYZING IRREGULAR TIME SERIES DATA](https://openreview.net/forum?id=4VIgNuQ1pY) | ICLR |
| 2024 | [](https://openreview.net/pdf?id=O8ouVV8PjF)[CNN KERNELS CAN BE THE BEST SHAPELETS](https://openreview.net/pdf?id=O8ouVV8PjF) | ICLR |
| 2024 | [](https://openreview.net/forum?id=WS7GuBDFa2)[LEARNING TO EMBED TIME SERIES PATCHES INDEPENDENTLY](https://openreview.net/forum?id=WS7GuBDFa2) | ICLR |
| 2024 | [](https://openreview.net/forum?id=MJksrOhurE)[CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting](https://openreview.net/forum?id=MJksrOhurE) | ICLR |
| 2024 | [](https://openreview.net/forum?id=vpJMJerXHU)[MODERNTCN: A MODERN PURE CONVOLUTION STRUCTURE FOR GENERAL TIME SERIES ANALYSIS](https://openreview.net/forum?id=vpJMJerXHU) | ICLR |
| 2024 | [](https://openreview.net/pdf?id=aR3uxWlZhX)[UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis](https://openreview.net/pdf?id=aR3uxWlZhX) | ICML |
| 2024 | [](https://openreview.net/forum?id=CGR3vpX63X)[TSLANet: Rethinking Transformers for Time Series Representation Learning](https://openreview.net/forum?id=CGR3vpX63X) | ICML |
| 2024 | [](https://openreview.net/forum?id=bYRYb7DMNo)[Timer: Generative Pre-trained Transformers Are Large Time Series Models](https://openreview.net/forum?id=bYRYb7DMNo) | ICML |
| 2024 | [](https://openreview.net/forum?id=ecO7WOIlMD)[MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series](https://openreview.net/forum?id=ecO7WOIlMD) | ICML |
| 2024 | [](https://openreview.net/forum?id=FVvf69a5rx)[MOMENT: A Family of Open Time-series Foundation Models](https://openreview.net/forum?id=FVvf69a5rx) | ICML |
| 2024 | [Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask](https://arxiv.org/abs/2405.05959) | KDD |
### Learning-Focused Approaches
> Studies in this category focus on devising novel objective functions or pretext tasks used for the representation learning process, i.e., model training. The learning objectives can be categorized into supervised, unsupervised, or self-supervised learning, depending on the use of labeled instances. The difference between unsupervised and self-supervised learning is the presence of pseudo labels. Specifically, unsupervised learning is based on the reconstruction of its input, while self-supervised learning uses pseudo labels as self-supervision signals.
| Year | Title | Venue |
| ---- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------- |
| 2018 | [](https://proceedings.mlr.press/v84/wu18b.html)[Random Warping Series: A Random Features Method for Time-Series Embedding](https://proceedings.mlr.press/v84/wu18b.html) | AISTATS |
| 2018 | [](https://link.springer.com/chapter/10.1007/978-3-030-10928-8_34)[Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint](https://link.springer.com/chapter/10.1007/978-3-030-10928-8_34) | ECML PKDD |
| 2019 | [](https://link.springer.com/chapter/10.1007/978-3-030-46133-1_19)[Learning Disentangled Representations of Satellite Image Time Series](https://link.springer.com/chapter/10.1007/978-3-030-46133-1_19) | ECML PKDD |
| 2019 | [](https://www.sciencedirect.com/science/article/pii/S092523121830626X)[Wave2Vec: Deep representation learning for clinical temporal data](https://www.sciencedirect.com/science/article/pii/S092523121830626X) | Neurocomputing |
| 2019 | [](https://proceedings.neurips.cc/paper_files/paper/2019/hash/53c6de78244e9f528eb3e1cda69699bb-Abstract.html)[Unsupervised Scalable Representation Learning for Multivariate Time Series](https://proceedings.neurips.cc/paper_files/paper/2019/hash/53c6de78244e9f528eb3e1cda69699bb-Abstract.html) | NeurIPS |
| 2019 | [](https://ieeexplore.ieee.org/document/8736337)[Audio Word2vec: Sequence-to-Sequence Autoencoding for Unsupervised Learning of Audio Segmentation and Representation](https://ieeexplore.ieee.org/document/8736337) | IEEE/ACM TASLP |
| 2020 | [](https://arxiv.org/abs/2010.01596)[TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series](https://arxiv.org/abs/2010.01596) | arXiv |
| 2020 | [](https://proceedings.neurips.cc/paper/2020/hash/328e5d4c166bb340b314d457a208dc83-Abstract.html)[Learning Representations from Audio-Visual Spatial Alignment](https://proceedings.neurips.cc/paper/2020/hash/328e5d4c166bb340b314d457a208dc83-Abstract.html) | NeurIPS |
| 2020 | [](https://proceedings.neurips.cc/paper/2020/hash/5c9452254bccd24b8ad0bb1ab4408ad1-Abstract.html)[Cycle-Contrast for Self-Supervised Video Representation Learning](https://proceedings.neurips.cc/paper/2020/hash/5c9452254bccd24b8ad0bb1ab4408ad1-Abstract.html) | NeurIPS |
| 2020 | [](https://ieeexplore.ieee.org/document/8685795)[End-to-End Incomplete Time-Series Modeling From Linear Memory of Latent Variables](https://ieeexplore.ieee.org/document/8685795) | IEEE TCYB |
| 2020 | [](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30)[Self-supervised Video Representation Learning by Pace Prediction](https://link.springer.com/chapter/10.1007/978-3-030-58520-4_30) | ECCV |
| 2020 | [](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19)[Memory-Augmented Dense Predictive Coding for Video Representation Learning](https://link.springer.com/chapter/10.1007/978-3-030-58580-8_19) | ECCV |
| 2021 | [](https://openreview.net/forum?id=8qDwejCuCN)[Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding](https://openreview.net/forum?id=8qDwejCuCN) | ICLR |
| 2021 | [](https://openaccess.thecvf.com/content/ICCV2021/html/Behrmann_Long_Short_View_Feature_Decomposition_via_Contrastive_Video_Representation_Learning_ICCV_2021_paper.html)[Long Short View Feature Decomposition via Contrastive Video Representation Learning](https://openaccess.thecvf.com/content/ICCV2021/html/Behrmann_Long_Short_View_Feature_Decomposition_via_Contrastive_Video_Representation_Learning_ICCV_2021_paper.html) | ICCV |
| 2021 | [](https://dl.acm.org/doi/10.1145/3447548.3467401)[A Transformer-based Framework for Multivariate Time Series Representation Learning](https://dl.acm.org/doi/10.1145/3447548.3467401) | KDD |
| 2021 | [](https://proceedings.neurips.cc/paper_files/paper/2021/hash/51200d29d1fc15f5a71c1dab4bb54f7c-Abstract.html)[TriBERT: Human-centric Audio-visual Representation Learning](https://proceedings.neurips.cc/paper_files/paper/2021/hash/51200d29d1fc15f5a71c1dab4bb54f7c-Abstract.html) | NeurIPS |
| 2021 | [](https://openaccess.thecvf.com/content/CVPR2021/html/Haresh_Learning_by_Aligning_Videos_in_Time_CVPR_2021_paper.html)[Learning by aligning videos in time](https://openaccess.thecvf.com/content/CVPR2021/html/Haresh_Learning_by_Aligning_Videos_in_Time_CVPR_2021_paper.html) | CVPR |
| 2021 | [](https://openaccess.thecvf.com/content/CVPR2021/html/Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper.html)[Representation Learning via Global Temporal Alignment and Cycle-Consistency](https://openaccess.thecvf.com/content/CVPR2021/html/Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper.html) | CVPR |
| 2021 | [](https://openaccess.thecvf.com/content/CVPR2021/html/Qian_Spatiotemporal_Contrastive_Video_Representation_Learning_CVPR_2021_paper.html)[Spatiotemporal Contrastive Video Representation Learning](https://openaccess.thecvf.com/content/CVPR2021/html/Qian_Spatiotemporal_Contrastive_Video_Representation_Learning_CVPR_2021_paper.html) | CVPR |
| 2021 | [](https://openaccess.thecvf.com/content/ICCV2021/html/Jenni_Time-Equivariant_Contrastive_Video_Representation_Learning_ICCV_2021_paper.html)[Time-Equivariant Contrastive Video Representation Learning](https://openaccess.thecvf.com/content/ICCV2021/html/Jenni_Time-Equivariant_Contrastive_Video_Representation_Learning_ICCV_2021_paper.html) | ICCV |
| 2021 | [](https://www.ijcai.org/proceedings/2021/324)[Time-Series Representation Learning via Temporal and Contextual Contrasting](https://www.ijcai.org/proceedings/2021/324) | IJCAI |
| 2021 | [](https://ojs.aaai.org/index.php/AAAI/article/view/16189)[RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning](https://ojs.aaai.org/index.php/AAAI/article/view/16189) | AAAI |
| 2022 | [](https://sslneurips22.github.io/paper_pdfs/paper_74.pdf)[TS-Rep: Self-supervised time series representation learning from robot sensor data](https://sslneurips22.github.io/paper_pdfs/paper_74.pdf) | NeurIPS (Workshop) |
| 2022 | [](https://dl.acm.org/doi/10.1145/3534678.3539329)[TARNet : Task-Aware Reconstruction for Time-Series Transformer](https://dl.acm.org/doi/10.1145/3534678.3539329) | KDD |
| 2022 | [](https://www.sciencedirect.com/science/article/abs/pii/S0950705122002726)[TimeCLR: A self-supervised contrastive learning framework for univariate time series representation](https://www.sciencedirect.com/science/article/abs/pii/S0950705122002726) | KBS |
| 2022 | [](https://ieeexplore.ieee.org/document/9878692/)[Weakly Paired Associative Learning for Sound and Image Representations via Bimodal Associative Memory](https://ieeexplore.ieee.org/document/9878692/) | CVPR |
| 2022 | [](https://ojs.aaai.org/index.php/AAAI/article/view/20248)[Contrastive Spatio-Temporal Pretext Learning for Self-Supervised Video Representation](https://ojs.aaai.org/index.php/AAAI/article/view/20248) | AAAI |
| 2022 | [](https://dl.acm.org/doi/abs/10.1145/3503161.3547783)[Dual Contrastive Learning for Spatio-temporal Representation](https://dl.acm.org/doi/abs/10.1145/3503161.3547783) | MM |
| 2022 | [](https://openreview.net/forum?id=OJ4mMfGKLN)[Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://openreview.net/forum?id=OJ4mMfGKLN) | NeurIPS |
| 2022 | [](https://openreview.net/forum?id=nhtkdCvVLIh)[Self-Supervised Time Series Representation Learning with Temporal-Instance Similarity Distillation](https://openreview.net/forum?id=nhtkdCvVLIh) | ICML (Workshop) |
| 2022 | [](https://ojs.aaai.org/index.php/AAAI/article/view/20881)[TS2Vec: Towards Universal Representation of Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/20881) | AAAI |
| 2022 | [](https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Cross-Architecture_Self-Supervised_Video_Representation_Learning_CVPR_2022_paper.html)[Cross-Architecture Self-supervised Video Representation Learning](https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Cross-Architecture_Self-Supervised_Video_Representation_Learning_CVPR_2022_paper.html) | CVPR |
| 2022 | [](https://openaccess.thecvf.com/content/CVPR2022/html/Qing_Learning_From_Untrimmed_Videos_Self-Supervised_Video_Representation_Learning_With_Hierarchical_CVPR_2022_paper.html)[Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency](https://openaccess.thecvf.com/content/CVPR2022/html/Qing_Learning_From_Untrimmed_Videos_Self-Supervised_Video_Representation_Learning_With_Hierarchical_CVPR_2022_paper.html) | CVPR |
| 2022 | [](https://openaccess.thecvf.com/content/CVPR2022/html/Duan_TransRank_Self-Supervised_Video_Representation_Learning_via_Ranking-Based_Transformation_Recognition_CVPR_2022_paper.html)[TransRank: Self-supervised Video Representation Learning via Ranking-based Transformation Recognition](https://openaccess.thecvf.com/content/CVPR2022/html/Duan_TransRank_Self-Supervised_Video_Representation_Learning_via_Ranking-Based_Transformation_Recognition_CVPR_2022_paper.html) | CVPR |
| 2022 | [](https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Frame-Wise_Action_Representations_for_Long_Videos_via_Sequence_Contrastive_Learning_CVPR_2022_paper.html)[Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning](https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Frame-Wise_Action_Representations_for_Long_Videos_via_Sequence_Contrastive_Learning_CVPR_2022_paper.html) | CVPR |
| 2022 | [](https://bmvc2022.mpi-inf.mpg.de/0541.pdf)[On Temporal Granularity in Self-Supervised Video Representation Learning](https://bmvc2022.mpi-inf.mpg.de/0541.pdf) | BMVC |
| 2022 | [](https://ojs.aaai.org/index.php/AAAI/article/view/20047)[Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity](https://ojs.aaai.org/index.php/AAAI/article/view/20047) | AAAI |
| 2022 | [](https://openaccess.thecvf.com/content/WACV2022/html/Zhang_Hierarchically_Decoupled_Spatial-Temporal_Contrast_for_Self-Supervised_Video_Representation_Learning_WACV_2022_paper.html)[Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning](https://openaccess.thecvf.com/content/WACV2022/html/Zhang_Hierarchically_Decoupled_Spatial-Temporal_Contrast_for_Self-Supervised_Video_Representation_Learning_WACV_2022_paper.html) | WACV |
| 2022 | [](https://ieeexplore.ieee.org/abstract/document/9352025)[Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics](https://ieeexplore.ieee.org/abstract/document/9352025) | IEEE TPAMI |
| 2022 | [](https://ieeexplore.ieee.org/abstract/document/9713748)[TCGL: Temporal Contrastive Graph for Self-Supervised Video Representation Learning](https://ieeexplore.ieee.org/abstract/document/9713748) | IEEE TIP |
| 2022 | [](https://www.sciencedirect.com/science/article/pii/S1077314222000376)[TCLR: Temporal contrastive learning for video representation](https://www.sciencedirect.com/science/article/pii/S1077314222000376) | CVIU |
| 2023 | [](https://ojs.aaai.org/index.php/AAAI/article/view/25876)[PrimeNet: Pre-Training for Irregular Multivariate Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/25876) | AAAI |
| 2023 | [](https://openreview.net/forum?id=sOQBHlCmzp)[Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series](https://openreview.net/forum?id=sOQBHlCmzp) | NeurIPS |
| 2023 | [](https://openreview.net/forum?id=ginTcBUnL8)[SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling](https://openreview.net/forum?id=ginTcBUnL8) | NeurIPS |
| 2023 | [](https://arxiv.org/abs/2301.08871)[Ti-MAE: Self-Supervised Masked Time Series Autoencoders](https://arxiv.org/abs/2301.08871) | arXiv |
| 2023 | [](https://dl.acm.org/doi/10.1145/3583780.3614759)[A Co-training Approach for Noisy Time Series Learning](https://dl.acm.org/doi/10.1145/3583780.3614759) | CIKM |
| 2023 | [](https://openreview.net/forum?id=CIFOsnhZvON)[TempCLR: Temporal Alignment Representation with Contrastive Learning](https://openreview.net/forum?id=CIFOsnhZvON) | ICLR |
| 2023 | [](https://openreview.net/forum?id=l4CZCKXoSn)[FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space](https://openreview.net/forum?id=l4CZCKXoSn) | NeurIPS |
| 2023 | [](https://ojs.aaai.org/index.php/AAAI/article/view/25915)[Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling](https://ojs.aaai.org/index.php/AAAI/article/view/25915) | AAAI |
| 2023 | [](https://www.vldb.org/pvldb/vol17/p386-wang.pdf)[A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning](https://www.vldb.org/pvldb/vol17/p386-wang.pdf) | VLDB |
| 2023 | [](https://arxiv.org/abs/2310.11959)[A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis](https://arxiv.org/abs/2310.11959) | arXiv |
| 2023 | [](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr)[FEAT: A general framework for Feature-aware Multivariate Time-series Representation Learning](https://pdf.sciencedirectassets.com/271505/1-s2.0-S0950705123X00161/1-s2.0-S0950705123005403/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEF8aCXVzLWVhc3QtMSJIMEYCIQD5hn8Mnl3gtFxq1nUFNfN4AkRGLGc0J7neyhwNqErOWgIhAPaV8bgoej7W9FIVIXRJEs1dJ4VCrxcjrjGRBp%2BkzRHlKrIFCBgQBRoMMDU5MDAzNTQ2ODY1IgyE6HLDOC7UTDUg0pkqjwUea8WEnsKUVN0tQyNMW2CZ0gIGG2DL4zI%2FOhz0UeagODZquX58pQLjvbMX9m5ohgzmh3UXgw6NklEoIDfnrs9%2BmRAiI0kOhB3SKGMmpYneL5TfxmSpLp51G53KC9usiYRZcxWti99qQVxTizvMEB8aaidOw7buJpMFN5%2FqZqNED0VGiTjIXh37Vs7kWfZ93M5ADP27q%2F4RwvixEekWumRgoOZr4JmpCAhgIAvdxT3A4Va%2BO4IHfqloPut4mBeFQflTBaF1VXEWGY2nKQNfvnjOvpk6jWIBrIkE4PCkDjPQVglUCVFbtkYn37DoEM6qz5o%2FJxDNT3fxyDMsJyzPqV4WJ6W22FoHjkXF4zUEOsJpwBiuAdwBQy5ej6%2BItgLt7p4Q%2BkhYpn%2F4Zdw8pn%2BPrhKVoI5%2B2NmePKc9xu9XSvh%2B%2F7oVotJArmAmeNiSl%2BNf4R848MQ%2Bilmg5MeeHbIFWJc2QGxFDOTWKJsds9yIgMfZIVMUzd2anx4MHvT6lW4qdFKXb9nxZj0%2BANXq2dyCMMUD4Bg5q6OzYJZsRkK3p6Bwh80m%2BAIDIzy6ze2KKpoLYs3eDniVHtZJJNL%2FjFUepIVM0QpaYF5bj1fODRi4MUGE%2B%2BR56LZ4LJdDZTqQV%2B1De9VNPmBJV7%2B8OHjSdI48agJ7kKg%2B30Cradn3xXPkiL6cXuRxuePwCcacJ8sNyVVEPJXK2W2tKYH8IuVm4GdAM1Q%2BmYMxpJzDsPE08FNN8AZ6n4ezHtoWHdxay6WzaiWpLOEKYaoDRHALfTuwsen3FtEqXFECsPYlPjDR6tH4Nndku4pfjw90CI7es4ggMqUoxzU8cbbojdDG%2FmOaeKztu7fQqDJGD9Z3mY9jJYxNs1HBMMLQ46YGOrABXQddNJuWvlRrRFyMKs%2FyWrIKde1VETUwdPd5o6xsXQOIqXtJaAt5zbDTfYMXJ9Mz0KiEkkE5Dayt33ozxrR9KKzYxKUtsd67zB3EWac8De%2FOX62MGAQCXlvYlVijjVPosfJXFvB7zD%2BKIppS7RUYYDpNqcW8DOQUDIOuSnk1VG3fVCDU88o81OBr2ZF%2Btt4mo8FyTK%2BgX0gmeMG%2BP1B1eZwglxj82329ZvmAEFv11ns%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230813T153436Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYVHIZFQJK%2F20230813%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=edd52e1a7e3d9453d4620d4f1602656da03fadd4a82d3d5af1dc3e3ba8d45d32&hash=c29b8e0b4587df019b2d07326b7a707d3765b18585be1a37c4047e8a4f6c5ebf&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0950705123005403&tid=spdf-df89dd41-6329-4760-8dc8-84cd1d0071f0&sid=ea134a4167a8e14cbe1835c5dab4aa9ab4dcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=1113550654515750060a&rr=7f6205eede60c185&cc=kr) | KBS |
| 2023 | [](https://arxiv.org/abs/2306.06579)[Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach](https://arxiv.org/abs/2306.06579) | arXiv |
| 2023 | [](https://arxiv.org/abs/2303.01034)[Multi-Task Self-Supervised Time-Series Representation Learning](https://arxiv.org/abs/2303.01034) | arXiv |
| 2023 | [](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Modeling_Video_As_Stochastic_Processes_for_Fine-Grained_Video_Representation_Learning_CVPR_2023_paper.html)[Modeling Video As Stochastic Processes for Fine-Grained Video Representation Learning](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Modeling_Video_As_Stochastic_Processes_for_Fine-Grained_Video_Representation_Learning_CVPR_2023_paper.html) | CVPR |
| 2023 | [](https://openreview.net/pdf?id=EvGOdASdHi)[Context Consistency Regularization for Label Sparsity in Time Series](https://openreview.net/pdf?id=EvGOdASdHi) | ICML |
| 2024 | [](https://openreview.net/forum?id=3zQo5oUvia)[RETRIEVAL-BASED RECONSTRUCTION FOR TIME-SERIES CONTRASTIVE LEARNING](https://openreview.net/forum?id=3zQo5oUvia) | ICLR |
| 2024 | [](https://openreview.net/forum?id=Tuh4nZVb0g)[TEST: TEXT PROTOTYPE ALIGNED EMBEDDING TO ACTIVATE LLM’S ABILITY FOR TIME SERIES](https://openreview.net/forum?id=Tuh4nZVb0g) | ICLR |
| 2024 | [](https://arxiv.org/abs/2309.05305)[Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data](https://arxiv.org/abs/2309.05305) | AAAI |
| 2024 | [](https://arxiv.org/abs/2312.15709)[TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning](https://arxiv.org/abs/2312.15709) | AAAI |
| 2024 | [](https://openreview.net/forum?id=3y2TfP966N)[T-REP: REPRESENTATION LEARNING FOR TIME SERIES USING TIME-EMBEDDINGS](https://openreview.net/forum?id=3y2TfP966N) | ICLR |
| 2024 | [](https://arxiv.org/abs/2403.00131)[UNITS: A Unified Multi-Task Time Series Model](https://arxiv.org/abs/2403.00131) | arXiv |
| 2024 | [](https://www.sciencedirect.com/science/article/pii/S0031320323006416)[Multi-scale self-supervised representation learning with temporal alignment for multi-rate time series modeling](https://www.sciencedirect.com/science/article/pii/S0031320323006416) | Pattern Recognition |
| 2024 | [](https://openreview.net/forum?id=pAsQSWlDUf)[SOFT CONTRASTIVE LEARNING FOR TIME SERIES](https://openreview.net/forum?id=pAsQSWlDUf) | ICLR |
| 2024 | [](https://openreview.net/forum?id=WS7GuBDFa2)[LEARNING TO EMBED TIME SERIES PATCHES INDEPENDENTLY](https://openreview.net/forum?id=WS7GuBDFa2) | ICLR |
| 2024 | [](https://openreview.net/forum?id=MJksrOhurE)[CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting](https://openreview.net/forum?id=MJksrOhurE) | ICLR |
| 2024 | [](https://openreview.net/pdf?id=aR3uxWlZhX)[UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis](https://openreview.net/pdf?id=aR3uxWlZhX) | ICML |
| 2024 | [](https://openreview.net/forum?id=bYRYb7DMNo)[Timer: Generative Pre-trained Transformers Are Large Time Series Models](https://openreview.net/forum?id=bYRYb7DMNo) | ICML |
| 2024 | [](https://openreview.net/forum?id=Rx9GMufByc)[Multi-Patch Prediction: Adapting Language Models for Time Series Representation Learning](https://openreview.net/forum?id=Rx9GMufByc) | ICML |
| 2024 | [](https://openreview.net/forum?id=ecO7WOIlMD)[MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series](https://openreview.net/forum?id=ecO7WOIlMD) | ICML |
| 2024 | [](https://openreview.net/forum?id=wrTzLoqbCg)[TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling](https://openreview.net/forum?id=wrTzLoqbCg) | ICML |
| 2024 | [Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask](https://arxiv.org/abs/2405.05959) | KDD |
### Data-Centric Approaches
> In this group, we categorize the methods that focus on finding a new way to enhance the usefulness of the training data at hand. Data-centric approaches prioritize engineering the data itself rather than focusing on model architecture and loss function design. We categorize data-centric approaches into three techniques: data augmentation, decomposition and transformation, and sample selection.
| Year | Title | Venue |
| ---- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------- |
| 2018 | [](https://dl.acm.org/doi/abs/10.1145/3219819.3220060)[Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis](https://dl.acm.org/doi/abs/10.1145/3219819.3220060) | KDD |
| 2019 | [](https://proceedings.neurips.cc/paper_files/paper/2019/hash/53c6de78244e9f528eb3e1cda69699bb-Abstract.html)[Unsupervised Scalable Representation Learning for Multivariate Time Series](https://proceedings.neurips.cc/paper_files/paper/2019/hash/53c6de78244e9f528eb3e1cda69699bb-Abstract.html) | NeurIPS |
| 2021 | [](https://openreview.net/forum?id=txWfwhc6gi)[Contrastive Learning of Global and Local Video Representations](https://openreview.net/forum?id=txWfwhc6gi) | NeurIPS |
| 2021 | [](https://www.sciencedirect.com/science/article/pii/S0020025520312287)[A deep multi-task representation learning method for time series classification and retrieval](https://www.sciencedirect.com/science/article/pii/S0020025520312287) | Information Sciences |
| 2021 | [](https://www.sciencedirect.com/science/article/pii/S0950705120306808)[DeLTa: Deep local pattern representation for time-series clustering and classification using visual perception](https://www.sciencedirect.com/science/article/pii/S0950705120306808) | KBS |
| 2022 | [](https://arxiv.org/abs/2210.10630)[Irregularly-Sampled Time Series Modeling with Spline Networks](https://arxiv.org/abs/2210.10630) | ICML (Workshop) |
| 2022 | [](https://ieeexplore.ieee.org/document/9769928)[Multi-View Integrative Attention-Based Deep Representation Learning for Irregular Clinical Time-Series Data](https://ieeexplore.ieee.org/document/9769928) | IEEE JBHI |
| 2022 | [](https://www.sciencedirect.com/science/article/abs/pii/S0950705122002726)[TimeCLR: A self-supervised contrastive learning framework for univariate time series representation](https://www.sciencedirect.com/science/article/abs/pii/S0950705122002726) | KBS |
| 2022 | [](https://openreview.net/forum?id=OJ4mMfGKLN)[Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://openreview.net/forum?id=OJ4mMfGKLN) | NeurIPS |
| 2022 | [](https://ojs.aaai.org/index.php/AAAI/article/view/20210)[Cross-Modal Mutual Learning for Audio-Visual Speech Recognition and Manipulation](https://ojs.aaai.org/index.php/AAAI/article/view/20210) | AAAI |
| 2022 | [](https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Frame-Wise_Action_Representations_for_Long_Videos_via_Sequence_Contrastive_Learning_CVPR_2022_paper.html)[Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning](https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Frame-Wise_Action_Representations_for_Long_Videos_via_Sequence_Contrastive_Learning_CVPR_2022_paper.html) | CVPR |
| 2022 | [](https://openaccess.thecvf.com/content/WACV2022/html/Zhang_Hierarchically_Decoupled_Spatial-Temporal_Contrast_for_Self-Supervised_Video_Representation_Learning_WACV_2022_paper.html)[Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning](https://openaccess.thecvf.com/content/WACV2022/html/Zhang_Hierarchically_Decoupled_Spatial-Temporal_Contrast_for_Self-Supervised_Video_Representation_Learning_WACV_2022_paper.html) | WACV |
| 2022 | [](https://proceedings.mlr.press/v162/yang22e.html)[Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion](https://proceedings.mlr.press/v162/yang22e.html) | ICML |
| 2023 | [](https://dl.acm.org/doi/10.1145/3583780.3614759)[A Co-training Approach for Noisy Time Series Learning](https://dl.acm.org/doi/10.1145/3583780.3614759) | CIKM |
| 2023 | [](https://ojs.aaai.org/index.php/AAAI/article/view/25575)[Time Series Contrastive Learning with Information-Aware Augmentations](https://ojs.aaai.org/index.php/AAAI/article/view/25575) | AAAI |
| 2023 | [](https://openreview.net/forum?id=dbVRDk2wt7)[Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning](https://openreview.net/forum?id=dbVRDk2wt7) | NeurIPS |
| 2023 | [](https://openreview.net/forum?id=fxjzKOdw9wb)[Exploring Temporally Dynamic Data Augmentation for Video Recognition](https://openreview.net/forum?id=fxjzKOdw9wb) | ICLR |
| 2023 | [](https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Learning_Video_Representations_From_Large_Language_Models_CVPR_2023_paper.html)[Learning Video Representations From Large Language Models](https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Learning_Video_Representations_From_Large_Language_Models_CVPR_2023_paper.html) | CVPR |
| 2023 | [](https://ojs.aaai.org/index.php/AAAI/article/view/25915)[Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling](https://ojs.aaai.org/index.php/AAAI/article/view/25915) | AAAI |
| 2023 | [](https://arxiv.org/abs/2310.11959)[A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis](https://arxiv.org/abs/2310.11959) | arXiv |
| 2023 | [](https://arxiv.org/abs/2306.06579)[Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach](https://arxiv.org/abs/2306.06579) | arXiv |
| 2023 | [](https://openreview.net/forum?id=5lgD4vU-l24s)[Recursive Time Series Data Augmentation](https://openreview.net/forum?id=5lgD4vU-l24s) | ICLR |
| 2023 | [](https://openreview.net/pdf?id=EvGOdASdHi)[Context Consistency Regularization for Label Sparsity in Time Series](https://openreview.net/pdf?id=EvGOdASdHi) | ICML |
| 2023 | [](https://openreview.net/forum?id=Qamz7Q_Ta1k)[Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs](https://openreview.net/forum?id=Qamz7Q_Ta1k) | ICLR |
| 2023 | [](https://ojs.aaai.org/index.php/AAAI/article/view/25194)[Frequency Selective Augmentation for Video Representation Learning](https://ojs.aaai.org/index.php/AAAI/article/view/25194) | AAAI |
| 2024 | [](https://openreview.net/forum?id=bWcnvZ3qMb)[FITS: MODELING TIME SERIES WITH 10k PARAMETERS](https://openreview.net/forum?id=bWcnvZ3qMb) | ICLR |
| 2024 | [](https://arxiv.org/abs/2312.15709)[TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning](https://arxiv.org/abs/2312.15709) | AAAI |
| 2024 | [](https://openreview.net/forum?id=EIPLdFy3vp)[PARAMETRIC AUGMENTATION FOR TIME SERIES CONTRASTIVE LEARNING](https://openreview.net/forum?id=EIPLdFy3vp) | ICLR |
| 2024 | [](https://openreview.net/forum?id=bYRYb7DMNo)[Timer: Generative Pre-trained Transformers Are Large Time Series Models](https://openreview.net/forum?id=bYRYb7DMNo) | ICML |
| 2024 | [](https://openreview.net/forum?id=ecO7WOIlMD)[MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series](https://openreview.net/forum?id=ecO7WOIlMD) | ICML |
| 2024 | [](https://openreview.net/forum?id=FVvf69a5rx)[MOMENT: A Family of Open Time-series Foundation Models](https://openreview.net/forum?id=FVvf69a5rx) | ICML |
## Neighbor Repositories
- https://github.com/qingsongedu/awesome-AI-for-time-series-papers
- https://github.com/qianlima-lab/time-series-ptms
- https://github.com/qingsongedu/time-series-transformers-review
- https://github.com/lixus7/Time-Series-Works-Conferences
- https://github.com/qingsongedu/Awesome-TimeSeries-AIOps-LM-LLM
- https://github.com/qingsongedu/Awesome-SSL4TS