Awesome Deep Time-Series Representations

This repository is intended to help readers interested in learning universal representations of time series with deep learning.

Since the paper has been accepted by ACM Computing Surveys as the definitive version of record, we will update this repository regularly, in line with top-tier conference publication cycles, to keep it up to date through NeurIPS 2026. After that, if your paper is missing or you have other requests, please open an issue, submit a pull request, or contact patara.t@kaist.ac.kr

Next Batch: IJCAI 2025, ICDM 2025, ICDE 2025, CIKM 2025, KDD 2025, ICML 2025, NeurIPS 2025 → NeurIPS 2026.

Accompanying Paper: Universal Time-Series Representation Learning: A Survey, Extended Version on arXiv.

@article{trirat2026universal,
  author = {Trirat, Patara and Shin, Yooju and Kang, Junhyeok and Nam, Youngeun and Na, Jihye and Bae, Minyoung and Kim, Joeun and Kim, Byunghyun and Lee, Jae-Gil},
  title = {Universal Time-Series Representation Learning: A Survey},
  year = {2026},
  issue_date = {September 2026},
  volume = {58},
  number = {12},
  doi = {10.1145/3817600},
  journal = {ACM Computing Surveys},
  month = jun,
  articleno = {321},
  numpages = {40}
}

Proposed Taxonomy

Contents

• Related Survey Papers
  • Time-Series Data Mining and Analysis
  • General Representation Learning
• Research Papers
  • Neural Architectural Approaches
  • Learning-Focused Approaches
  • Data-Centric Approaches
• Other Repositories

Related Surveys (Latest Update: May, 2026)

Time-Series Data Mining and Analysis

Title	Affiliation	Venue	Year
Discrete Wavelet Transform-based Time Series Analysis and Mining	University of Maryland	ACM CSUR	2011
Time-Series Data Mining	IRCAM	ACM CSUR	2012
A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling	Örebro University	Pattern Recognition Letters	2014
Time-series clustering – A decade review	University of Malaya	Information Systems	2015
Deep Learning for Time-Series Analysis	University of Kaiserslautern	arXiv	2017
A survey of methods for time series change point detection	Washington State University	KAIS	2017
Survey on time series motif discovery	Ostwestfalen-Lippe University of Applied Sciences	WIDM	2017
Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review	Ecole Nationale des Sciences de l’Informatique	MDPI Applied Sciences	2019
Deep learning for time series classification: a review	Université Haute Alsace	Data Mining and Knowledge Discovery	2019
Anomaly Detection for IoT Time-Series Data: A Survey	University of Keele	IEEE IoT-J	2019
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data	Peking University	arXiv	2020
Approaches and Applications of Early Classification of Time Series: A Review	Indian Institute of Technology (BHU) Varanasi	IEEE TAI	2020
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series	University of Massachusetts Amherst	NeurIPS Workshop on ML-RSA	2020
A Review of Deep Learning Models for Time Series Prediction	Dalian University of Technology	IEEE Sensors Journal	2021
An empirical survey of data augmentation for time series classification with neural networks	Kyushu University	PLOS ONE	2021
Time-series forecasting with deep learning: a survey	University of Oxford	Phil.Trans.R.Soc.A	2021
A Review on Outlier/Anomaly Detection in Time Series Data	Basque Research and Technology Alliance	ACM CSUR	2021
A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives	University of Newcastle	FICC	2021
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines	Seoul National University	IEEE Access	2021
Time Series Data Augmentation for Deep Learning: A Survey	Alibaba Group	IJCAI	2021
An Experimental Review on Deep Learning Architectures for Time Series Forecasting	University of Sevilla	IJNS	2021
Experimental Comparison and Survey of Twelve Time Series Anomaly Detection Algorithms	Verint	JAIR	2021
Causal inference for time series analysis: problems, methods and evaluation	Arizona State University	KAIS	2021
End-to-end deep representation learning for time series clustering: a comparative study	Université de Haute Alsace	Data Mining and Knowledge Discovery	2022
Survey and Evaluation of Causal Discovery Methods for Time Series	Université Grenoble Alpes	JAIR	2022
A Review of Recurrent Neural Network-Based Methods in Computational Physiology	University of Pittsburgh	IEEE TNNLS	2022
Deep Learning for Time Series Anomaly Detection: A Survey	Monash University	arXiv	2022
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey	Amazon Research	ACM CSUR	2022
Transformers in Time Series: A Survey	Alibaba Group	IJCAI	2023
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey	Monash University	arXiv	2023
Label-efficient Time Series Representation Learning: A Review	Nanyang Technological University	arXiv	2023
Neural Time Series Analysis with Fourier Transform: A Survey	Beijing Institute of Technology	arXiv	2023
A Survey on Dimensionality Reduction Techniques for Time-Series Data	University of Colorado Boulder	IEEE Access	2023
Long sequence time-series forecasting with deep learning: A survey	Southwest Jiaotong University	Information Fusion	2023
Data Augmentation techniques in time series domain: a survey and taxonomy	Universidad Politécnica de Madrid	Neural Computing & Applications	2023
Diffusion Models for Time Series Applications: A Survey	University of Sydney	arXiv	2023
A Survey on Time-Series Pre-Trained Models	South China University of Technology	arXiv	2023
Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review	RMIT University	MDPI Sensors	2023
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects	Zhejiang University	IEEE TPAMI	2024
Unsupervised Representation Learning for Time Series: A Review	Shandong University	arXiv	2023
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook	Monash University	arXiv	2023
Foundation Models for Time Series Analysis: A Tutorial and Survey	The Hong Kong University of Science and Technology	arXiv	2024
Large Language Models for Time Series: A Survey	University of California, San Diego	arxiv	2024
Empowering Time Series Analysis with Large Language Models: A Survey	University of Connecticut	arxiv	2024
A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model	Hong Kong University of Science and Technology	arxiv	2024
Position: What Can Large Language Models Tell Us about Time Series Analysis	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	Tsinghua University	IEEE TPAMI	2026
Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey	The Hong Kong University of Science and Technology (Guangzhou)	arXiv	2024
Multi-modal Time Series Analysis: A Tutorial and Survey	University of Connecticut	KDD	2025
Foundation Models for Time Series: A Survey	Dell Technologies	arXiv	2025
How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook	Georgia Institute of Technology	arXiv	2025
Out-of-Distribution Generalization in Time Series: A Survey	Southwest Jiaotong University	Information Fusion	2026
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models	Salesforce AI Research	arXiv	2025

Representation Learning

Title	Affiliation	Venue	Year
Representation Learning: A Review and New Perspectives	University of Montreal	IEEE TPAMI	2013
A Survey of Multi-View Representation Learning	Zhejiang University	IEEE TKDE	2019
Deep Multimodal Representation Learning: A Survey	Fuzhou University	IEEE Access	2019
A Survey on Representation Learning for User Modeling	University of Georgia	IJCAI	2020
A survey on deep geometry learning: From a representation perspective	Chinese Academy of Sciences	Computational Visual Media	2020
A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications	Xiamen University	IEEE Access	2020
Contrastive Representation Learning: A Framework and Review	Dublin City University	IEEE Access	2020
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data	RMIT University	arXiv	2022
Self-Supervised Representation Learning: Introduction, advances, and challenges	University of Edinburgh	IEEE Signal Processing Magazine	2022
A Brief Overview of Universal Sentence Representation Methods: A Linguistic View	KU Leuven	ACM CSUR	2022
Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding	Soochow University	ACM CSUR	2022
Evaluation Methods for Representation Learning: A Survey	University of Tokyo	IJCAI	2022
Self-Supervised Speech Representation Learning: A Review	Meta	IEEE JSTSP	2022
A Survey on Hypergraph Representation Learning	Università degli Studi di Torino	ACM CSUR	2023
Representation learning for knowledge fusion and reasoning in Cyber–Physical–Social Systems: Survey and perspectives	Hainan University	Information Fusion	2023
Survey of Deep Representation Learning for Speech Emotion Recognition	University of Southern Queensland	IEEE TAFFC	2023
Graph Representation Learning and Its Applications: A Survey	Catholic University of Korea	MDPI Sensors	2023
Graph Representation Learning Meets Computer Vision: A Survey	Xidian University	IEEE TAI	2023
A Comprehensive Survey on Deep Graph Representation Learning	Peking University	arXiv	2023
Dynamic Graph Representation Learning with Neural Networks: A Survey	University of Rouen Normandy	arXiv	2023
Multiscale Representation Learning for Image Classification: A Survey	Xidian University	IEEE TAI	2023
A Survey on Protein Representation Learning: Retrospect and Prospect	Westlake University	arXiv	2023

Research Papers (Latest Update: NeurIPS 2024)

Data-Centric Approaches

This group presents the methods that focus on finding a new way to enhance the usefulness of the training data at hand. These approaches prioritize engineering the data itself rather than focusing on model architecture and loss function design to capture the underlying patterns, trends, and relevant features within the time series. As in the figure, we categorize these data-centric approaches into two groups based on their objectives: improving data quality or increasing data quantity.

Year	Title	Venue
2018	Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis	KDD
2019	Unsupervised Scalable Representation Learning for Multivariate Time Series	NeurIPS
2021	A deep multi-task representation learning method for time series classification and retrieval	Information Sciences
2021	Contrastive Learning of Global and Local Video Representations	NeurIPS
2021	DeLTa: Deep local pattern representation for time-series clustering and classification using visual perception	KBS
2022	Cross-Modal Mutual Learning for Audio-Visual Speech Recognition and Manipulation	AAAI
2022	Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning	CVPR
2022	Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning	WACV
2022	Irregularly-Sampled Time Series Modeling with Spline Networks	ICML (Workshop)
2022	Multi-View Integrative Attention-Based Deep Representation Learning for Irregular Clinical Time-Series Data	IEEE JBHI
2022	Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency	NeurIPS
2022	TimeCLR: A self-supervised contrastive learning framework for univariate time series representation	KBS
2022	Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion	ICML
2023	A Co-training Approach for Noisy Time Series Learning	CIKM
2023	Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling	AAAI
2023	A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis	VLDB
2023	Exploring Temporally Dynamic Data Augmentation for Video Recognition	ICLR
2023	Frequency Selective Augmentation for Video Representation Learning	AAAI
2023	Learning Video Representations From Large Language Models	CVPR
2023	Recursive Time Series Data Augmentation	ICLR
2023	Time Series Contrastive Learning with Information-Aware Augmentations	AAAI
2023	Context Consistency Regularization for Label Sparsity in Time Series	ICML
2023	Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning	NeurIPS
2024	Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder	ICDM
2024	FITS: MODELING TIME SERIES WITH 10k PARAMETERS	ICLR
2024	TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning	AAAI
2024	PARAMETRIC AUGMENTATION FOR TIME SERIES CONTRASTIVE LEARNING	ICLR
2024	Timer: Generative Pre-trained Transformers Are Large Time Series Models	ICML
2024	MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series	ICML
2024	MOMENT: A Family of Open Time-series Foundation Models	ICML
2024	UniCL: A Universal Contrastive Learning Framework for Large Time Series Models	arXiv
2024	Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis	NeurIPS
2024	Time-Series Representation Learning via Dual Reference Contrasting	CIKM
2025	ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data	AAAI
2025	Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning	AAAI
2025	Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals	WWW
2025	TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis	ICLR

Neural Architectural Approaches

As neural architectures play a crucial role in the quality of representations, this group examines novel network architecture designs aimed at enhancing representation learning. These improvements (depicted in the figure) include, for example, better temporal modeling, handling missing values and irregularities, and extracting inter-variable dependencies in multivariate time series.

Year	Title	Venue
2018	Learning representations of multivariate time series with missing data	Pattern Recognition
2018	Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis	KDD
2019	Audio Word2vec: Sequence-to-Sequence Autoencoding for Unsupervised Learning of Audio Segmentation and Representation	IEEE/ACM TASLP
2019	Latent ODEs for Irregularly-Sampled Time Series	NeurIPS
2019	Learning Disentangled Representations of Satellite Image Time Series	ECML PKDD
2019	Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks	CIKM
2019	Unsupervised Scalable Representation Learning for Multivariate Time Series	NeurIPS
2020	A real-time action representation with temporal encoding and deep compression	IEEE TCSVT
2020	End-to-End Incomplete Time-Series Modeling From Linear Memory of Latent Variables	IEEE TCYB
2020	Memory-Augmented Dense Predictive Coding for Video Representation Learning	ECCV
2020	Temporal Aggregate Representations for Long-Range Video Understanding	ECCV
2021	A deep multi-task representation learning method for time series classification and retrieval	Information Sciences
2021	A Transformer-based Framework for Multivariate Time Series Representation Learning	KDD
2021	Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting	ICDM
2021	DeLTa: Deep local pattern representation for time-series clustering and classification using visual perception	KBS
2021	Multi-Time Attention Networks for Irregularly Sampled Time Series	ICLR
2021	SSAN: Separable Self-Attention Network for Video Representation Learning	CVPR
2021	TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data	IJCAI
2021	Time Series Domain Adaptation via Sparse Associative Structure Alignment	AAAI
2021	TriBERT: Human-centric Audio-visual Representation Learning	NeurIPS
2022	CrossPyramid: Neural Ordinary Differential Equations Architecture for Partially-observed Time-series	arXiv
2022	Decoupling Local and Global Representations of Time Series	AISTATS
2022	EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting	WWW
2022	HyperTime: Implicit Neural Representations for Time Series	NeurIPS (Workshop)
2022	MARINA: An MLP-Attention Model for Multivariate Time-Series Analysis	CIKM
2022	Modeling Irregular Time Series with Continuous Recurrent Units	ICML
2022	TARNet : Task-Aware Reconstruction for Time-Series Transformer	KDD
2022	TCGL: Temporal Contrastive Graph for Self-Supervised Video Representation Learning	IEEE TIP
2022	Towards Learning Disentangled Representations for Time Series	KDD
2022	Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion	ICML
2022	Weakly Paired Associative Learning for Sound and Image Representations via Bimodal Associative Memory	CVPR
2023	A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning	VLDB
2023	Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series	NeurIPS
2023	ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling	NeurIPS
2023	TriD-MAE: A Generic Pre-trained Model for Multivariate Time Series with Missing Values	CIKM
2023	A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis	VLDB
2023	One Fits All: Universal Time Series Analysis by Pretrained LM and Specially Designed Adaptors	arXiv
2023	Effectively Modeling Time Series with Simple Discrete State Spaces	ICLR
2023	FEAT: A general framework for Feature-aware Multivariate Time-series Representation Learning	KBS
2023	Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion	ICML
2023	Multi-Task Self-Supervised Time-Series Representation Learning	arXiv
2023	Multivariate Time Series Representation Learning via Hierarchical Correlation Pooling Boosted Graph Neural Network	IEEE TAI
2023	Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series	ICML
2023	One Fits All: Power General Time Series Analysis by Pretrained LM	NeurIPS
2023	One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data	arXiv
2023	Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations	arXiv
2023	TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis	ICLR
2023	UniTS: A Universal Time Series Analysis Framework with Self-supervised Representation Learning	arXiv
2023	WHEN: A Wavelet-DTW Hybrid Attention Network for Heterogeneous Time Series Analysis	KDD
2023	Sparse Binary Transformers for Multivariate Time Series Modeling	KDD
2024	Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder	ICDM
2024	FITS: MODELING TIME SERIES WITH 10k PARAMETERS	ICLR
2024	NEWTIME: NUMERICALLY MULTI-SCALED EMBEDDING FOR LARGE-SCALE TIME SERIES PRETRAINING	arXiv
2024	T-REP: REPRESENTATION LEARNING FOR TIME SERIES USING TIME-EMBEDDINGS	ICLR
2024	STABLE NEURAL STOCHASTIC DIFFERENTIAL EQUATIONS IN ANALYZING IRREGULAR TIME SERIES DATA	ICLR
2024	CNN KERNELS CAN BE THE BEST SHAPELETS	ICLR
2024	GAFORMER: ENHANCING TIMESERIES TRANSFORMERS THROUGH GROUP-AWARE EMBEDDINGS	ICLR
2024	CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting	ICLR
2024	Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data	AAAI
2024	UNITS: A Unified Multi-Task Time Series Model	NeurIPS
2024	MODERNTCN: A MODERN PURE CONVOLUTION STRUCTURE FOR GENERAL TIME SERIES ANALYSIS	ICLR
2024	UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis	ICML
2024	TSLANet: Rethinking Transformers for Time Series Representation Learning	ICML
2024	MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series	ICML
2024	MOMENT: A Family of Open Time-series Foundation Models	ICML
2024	UniCL: A Universal Contrastive Learning Framework for Large Time Series Models	arXiv
2024	Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask	KDD
2024	Large Pre-trained time series models for cross-domain Time series analysis tasks	NeurIPS
2024	Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models	NeurIPS
2024	Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis	NeurIPS
2024	Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representation	NeurIPS
2024	Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series	NeurIPS
2024	Time-Series Representation Learning via Dual Reference Contrasting	CIKM
2024	iHyperTime: Interpretable Time Series Generation with Implicit Neural Representations	TMLR
2025	ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data	AAAI
2025	Federated Foundation Models on Heterogeneous Time Series	AAAI
2025	Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals	WWW
2025	TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis	ICLR
2025	TVNet: A Novel Time Series Analysis Method Based on Dynamic Convolution and 3D-Variation	ICLR

Learning-Focused Approaches

Studies in this category center on devising novel learning objective functions for the representation learning process, i.e., model (pre-)training. As in the figure, these studies can be classified into three groups based on the learning objectives: task-adaptive, non-contrasting, and contrasting losses.

Year	Title	Venue
2018	Random Warping Series: A Random Features Method for Time-Series Embedding	AISTATS
2018	Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint	ECML PKDD
2019	Audio Word2vec: Sequence-to-Sequence Autoencoding for Unsupervised Learning of Audio Segmentation and Representation	IEEE/ACM TASLP
2019	Learning Disentangled Representations of Satellite Image Time Series	ECML PKDD
2019	Unsupervised Scalable Representation Learning for Multivariate Time Series	NeurIPS
2019	Wave2Vec: Deep representation learning for clinical temporal data	Neurocomputing
2020	Cycle-Contrast for Self-Supervised Video Representation Learning	NeurIPS
2020	End-to-End Incomplete Time-Series Modeling From Linear Memory of Latent Variables	IEEE TCYB
2020	Learning Representations from Audio-Visual Spatial Alignment	NeurIPS
2020	Memory-Augmented Dense Predictive Coding for Video Representation Learning	ECCV
2020	Self-supervised Video Representation Learning by Pace Prediction	ECCV
2020	TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series	arXiv
2021	A Transformer-based Framework for Multivariate Time Series Representation Learning	KDD
2021	Learning by aligning videos in time	CVPR
2021	Long Short View Feature Decomposition via Contrastive Video Representation Learning	ICCV
2021	Representation Learning via Global Temporal Alignment and Cycle-Consistency	CVPR
2021	RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning	AAAI
2021	Spatiotemporal Contrastive Video Representation Learning	CVPR
2021	Time-Equivariant Contrastive Video Representation Learning	ICCV
2021	Time-Series Representation Learning via Temporal and Contextual Contrasting	IJCAI
2021	TriBERT: Human-centric Audio-visual Representation Learning	NeurIPS
2021	Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding	ICLR
2022	Contrastive Spatio-Temporal Pretext Learning for Self-Supervised Video Representation	AAAI
2022	Cross-Architecture Self-supervised Video Representation Learning	CVPR
2022	Dual Contrastive Learning for Spatio-temporal Representation	MM
2022	Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning	CVPR
2022	Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning	WACV
2022	Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency	CVPR
2022	On Temporal Granularity in Self-Supervised Video Representation Learning	BMVC
2022	Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency	NeurIPS
2022	Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity	AAAI
2022	Self-Supervised Time Series Representation Learning with Temporal-Instance Similarity Distillation	ICML (Workshop)
2022	Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics	IEEE TPAMI
2022	TARNet: Task-Aware Reconstruction for Time-Series Transformer	KDD
2022	TCGL: Temporal Contrastive Graph for Self-Supervised Video Representation Learning	IEEE TIP
2022	TCLR: Temporal contrastive learning for video representation	CVIU
2022	TimeCLR: A self-supervised contrastive learning framework for univariate time series representation	KBS
2022	TransRank: Self-supervised Video Representation Learning via Ranking-based Transformation Recognition	CVPR
2022	TS-Rep: Self-supervised time series representation learning from robot sensor data	NeurIPS (Workshop)
2022	TS2Vec: Towards Universal Representation of Time Series	AAAI
2022	Weakly Paired Associative Learning for Sound and Image Representations via Bimodal Associative Memory	CVPR
2023	A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning	VLDB
2023	Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series	NeurIPS
2023	A Co-training Approach for Noisy Time Series Learning	CIKM
2023	Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling	AAAI
2023	A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis	VLDB
2023	FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space	NeurIPS
2023	FEAT: A general framework for Feature-aware Multivariate Time-series Representation Learning	KBS
2023	Modeling Video As Stochastic Processes for Fine-Grained Video Representation Learning	CVPR
2023	Multi-Task Self-Supervised Time-Series Representation Learning	arXiv
2023	PrimeNet: Pre-Training for Irregular Multivariate Time Series	AAAI
2023	SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling	NeurIPS
2023	TempCLR: Temporal Alignment Representation with Contrastive Learning	ICLR
2023	Ti-MAE: Self-Supervised Masked Time Series Autoencoders	arXiv
2023	Context Consistency Regularization for Label Sparsity in Time Series	ICML
2024	Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder	ICDM
2024	T-REP: REPRESENTATION LEARNING FOR TIME SERIES USING TIME-EMBEDDINGS	ICLR
2024	TEST: TEXT PROTOTYPE ALIGNED EMBEDDING TO ACTIVATE LLM’S ABILITY FOR TIME SERIES	ICLR
2024	TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning	AAAI
2024	Multi-scale self-supervised representation learning with temporal alignment for multi-rate time series modeling	Pattern Recognition
2024	SOFT CONTRASTIVE LEARNING FOR TIME SERIES	ICLR
2024	LEARNING TO EMBED TIME SERIES PATCHES INDEPENDENTLY	ICLR
2024	RETRIEVAL-BASED RECONSTRUCTION FOR TIME-SERIES CONTRASTIVE LEARNING	ICLR
2024	CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting	ICLR
2024	Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data	AAAI
2024	UNITS: A Unified Multi-Task Time Series Model	NeurIPS
2024	UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis	ICML
2024	Timer: Generative Pre-trained Transformers Are Large Time Series Models	ICML
2024	Multi-Patch Prediction: Adapting Language Models for Time Series Representation Learning	ICML
2024	MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series	ICML
2024	TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling	ICML
2024	UniCL: A Universal Contrastive Learning Framework for Large Time Series Models	arXiv
2024	Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask	KDD
2024	Large Pre-trained time series models for cross-domain Time series analysis tasks	NeurIPS
2024	Time-Series Representation Learning via Dual Reference Contrasting	CIKM
2025	ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data	AAAI
2025	Federated Foundation Models on Heterogeneous Time Series	AAAI
2025	Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning	AAAI
2025	TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysis	AAAI
2025	DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis	AAAI
2025	Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals	WWW
2025	Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series	ICLR

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