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Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

2023-06-16 · Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan

Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance. Compared with many published self-supervised surveys on computer vision and natural language processing, a comprehensive survey for time series SSL is still missing. To fill this gap, we review current state-of-the-art SSL methods for time series data in this article. To this end, we first comprehensively review existing surveys related to SSL and time series, and then provide a new taxonomy of existing time series SSL methods by summarizing them from three perspectives: generative-based, contrastive-based, and adversarial-based. These methods are further divided into ten subcategories with detailed reviews and discussions about their key intuitions, main frameworks, advantages and disadvantages. To facilitate the experiments and validation of time series SSL methods, we also summarize datasets commonly used in time series forecasting, classification, anomaly detection, and clustering tasks. Finally, we present the future directions of SSL for time series analysis.

📄 PDF Abstract BibTeX arXiv:2306.10125

Code (1)

qingsongedu/Awesome-SSL4TS 공식 구현

Tasks

Anomaly Detection Self-Supervised LearningTime SeriesTime Series Analysis Time Series Forecasting

A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges

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Time series anomaly detection presents various challenges due to the sequential and dynamic nature of time-dependent data. Traditional unsupervised methods frequently encounter difficulties in generalization, often overf…

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Data Augmentation Representation Learning Self-Supervised Learning Time Series+2

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SOTA Transformer and DNN short text sentiment classifiers report over 97% accuracy on narrow domains like IMDB movie reviews. Real-world performance is significantly lower because traditional models overfit benchmarks an…

Self-Supervised Learning Sentiment Analysis Time Series Time Series Analysis

Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark

2024-04-25 · Elizabeth Fons, Rachneet Kaur, Soham Palande, Zhen Zeng 외

Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more. In this paper…

Time Series Time Series Analysis