awesome-deep-time-series-representations
[ACM CSUR] A curated list of papers on universal representation learning for time series.
https://github.com/itouchz/awesome-deep-time-series-representations
Last synced: 20 days ago
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Related Surveys (Latest Update: April, 2024)
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Representation Learning
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
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Time-Series Data Mining and Analysis
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
- Data Augmentation techniques in time series domain: a survey and taxonomy
- A survey of methods for time series change point detection
- Deep learning for time series classification: a review
- A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
- Causal inference for time series analysis: problems, methods and evaluation
- End-to-end deep representation learning for time series clustering: a comparative study
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Related Surveys (Latest Update: July, 2024)
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Representation Learning
- Representation Learning: A Review and New Perspectives
- A Survey of Multi-View Representation Learning
- A Survey on Representation Learning for User Modeling
- Contrastive Representation Learning: A Framework and Review
- Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data
- Self-Supervised Representation Learning: Introduction, advances, and challenges
- Evaluation Methods for Representation Learning: A Survey
- Self-Supervised Speech Representation Learning: A Review
- Survey of Deep Representation Learning for Speech Emotion Recognition
- Graph Representation Learning and Its Applications: A Survey
- Graph Representation Learning Meets Computer Vision: A Survey
- A Comprehensive Survey on Deep Graph Representation Learning
- Dynamic Graph Representation Learning with Neural Networks: A Survey
- Multiscale Representation Learning for Image Classification: A Survey
- A Survey on Protein Representation Learning: Retrospect and Prospect
- A survey on deep geometry learning: From a representation perspective
- Deep Multimodal Representation Learning: A Survey
- A survey on deep geometry learning: From a representation perspective
- A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A Brief Overview of Universal Sentence Representation Methods: A Linguistic View
- Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding
- A Survey on Hypergraph Representation Learning
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- A survey on deep geometry learning: From a representation perspective
- Representation learning for knowledge fusion and reasoning in Cyber–Physical–Social Systems: Survey and perspectives
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Time-Series Data Mining and Analysis
- Deep Learning for Time-Series Analysis
- Survey on time series motif discovery - Lippe University of Applied Sciences | WIDM | 2017 |
- Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
- Anomaly Detection for IoT Time-Series Data: A Survey - J | 2019 |
- A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data
- A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series - RSA | 2020 |
- A Review of Deep Learning Models for Time Series Prediction
- Time-series forecasting with deep learning: a survey
- Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
- Time Series Data Augmentation for Deep Learning: A Survey
- Experimental Comparison and Survey of Twelve Time Series Anomaly Detection Algorithms
- Survey and Evaluation of Causal Discovery Methods for Time Series
- A Review of Recurrent Neural Network-Based Methods in Computational Physiology
- Deep Learning for Time Series Anomaly Detection: A Survey
- Transformers in Time Series: A Survey
- Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
- Label-efficient Time Series Representation Learning: A Review
- Neural Time Series Analysis with Fourier Transform: A Survey
- A Survey on Dimensionality Reduction Techniques for Time-Series Data
- Diffusion Models for Time Series Applications: A Survey
- A Survey on Time-Series Pre-Trained Models
- Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review
- Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
- Unsupervised Representation Learning for Time Series: A Review
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Categories
Research Papers (Latest Update: NeurIPS 2024)
494
Related Surveys (Latest Update: July, 2024)
434
Related Surveys (Latest Update: October, 2023)
127
Research Papers (Latest Update: KDD 2024)
123
Research Papers (Latest Update: NeurIPS 2023)
89
Related Surveys (Latest Update: June, 2024)
82
Research Papers (Latest Update: ICML 2024)
62
Research Papers (Latest Update: ICLR 2024)
56
Related Surveys (Latest Update: April, 2024)
47
Proposed Taxonomy
44
Related Surveys (Latest Update: May, 2024)
42
Relevant Studies on Open Challenges
19
Related Surveys (Latest Update: May, 2026)
6
Sub Categories
Time-Series Data Mining and Analysis
614
Learning-Focused Approaches
559
Neural Architectural Approaches
204
Representation Learning
124
Data-Centric Approaches
61
Neural Architecture Search (NAS)
5
Distribution Shifts and Adaptation
5
Time-Series Active Learning
4
Interpretability, Fairness, and Responsible Use
2
Multi-Modal Representation Learning
2
Reliable Data Augmentation
1