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Conference Paper
Towards Better Time-series Data Augmentation for Contrastive Learning
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- Authors
- Jang-Ho Choi, Moonyoung Chung, Taewhi Lee, Jiyong Kim
- Issue Date
- 2023-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2023, pp.1322-1324
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Contrastive learning is now a popular choice for representation learning in various domains, including image and natural language processing. However, contrastive learning for time-series data is relatively limited, due to its unrecognizable, high-dimensional temporal structures. It is still difficult to generate valid augmented views that are semantically accurate, despite the significant research advances in the field of time-series data augmentation. In this work, we survey recent works in time-series contrastive learning and propose a simple augmentation-agnostic technique that can effectively improve the fidelity of the augmented views.
- KSP Keywords
- Data Augmentation, High-dimensional, Natural Language Processing(NLP), Representation learning, Time series data