Time Series Data Augmentation for Deep Learning: A Survey
Open accessAbstract
Deep learning performs remarkably well on many time series analysis tasks recently. The superior performance of deep neural networks relies heavily on a large number of training data to avoid overfitting. However, the labeled data of many real-world time series applications may be limited such as classification in medical time series and anomaly detection in AIOps. As an effective way to enhance the size and quality of the training data, data augmentation is crucial to the successful application of deep learning models on time series data. In this paper, we systematically review different data augmentation methods for time series. We propose a taxonomy for the reviewed methods, and then provide a structured review for these methods by highlighting their strengths and limitations. We also empirically compare different data augmentation methods for different tasks including time series classification, anomaly detection, and forecasting. Finally, we discuss and highlight five future directions to provide useful research guidance.
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Both [45] and [46] empirically compare different data augmentation methods for time series classification tasks.
“Moreover, both [45] and [46] empirically compare different data augmentation methods for time series classification tasks.”
From Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects (2024) · cites this paper as [45]
[45] proposed a new taxonomy that divides the existing data augmentation techniques into basic and advanced approaches.
“[45] proposed a new taxonomy that divides the existing data augmentation techniques into basic and advanced approaches.”
From Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects (2024) · cites this paper as [45]
Some works comprehensively compare and study augmentation methods and further evaluate the performance on different tasks.
“Therefore, some works comprehensively compare and study the augmentation methods and further evaluate the performance on different tasks [45], [119]-[121].”
From Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects (2024) · cites this paper as [45]
The widely used methods for time series data augmentation include jitter, scaling, rotation, permutation, and warping.
“The widely used methods for time series data include jitter, scaling, rotation, permutation, and warping [45], [119]-[121], [194].”
From Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects (2024) · cites this paper as [45]
The selection of proper augmentations that well-fit time-series data is less explored and still an open problem.
“However, the selection of the proper augmentations that well-fit time-series data is less explored and still an open problem [53].”
From Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification (2023) · cites this paper as [53]
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- Authors
- Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., & Xu, H.
- Affiliations
- Alibaba Group (China) (CN)
- Twitter (United States) (US)
- Venue
- Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
- Published
- 2021-08
- Pages
- 4653-4660
- OpenAlex
- W3008872739
- Cited by
- 727
- References
- 5
- Citations by year
- 2026: 64 · 2025: 139 · 2024: 171 · 2023: 152 · 2022: 117 · 2021: 69 · 2020: 15
- FWCI
- 77.25
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- time series data augmentation, limited labeled data, time series classification, anomaly detection, time series forecasting, medical time series, AIOps, overfitting
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