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Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey
March 21, 2024, 4:43 a.m. | Zijun Gao, Lingbo Li
cs.LG updates on arXiv.org arxiv.org
Abstract: Data Augmentation (DA) has emerged as an indispensable strategy in Time Series Classification (TSC), primarily due to its capacity to amplify training samples, thereby bolstering model robustness, diversifying datasets, and curtailing overfitting. However, the current landscape of DA in TSC is plagued with fragmented literature reviews, nebulous methodological taxonomies, inadequate evaluative measures, and a dearth of accessible, user-oriented tools. In light of these challenges, this study embarks on an exhaustive dissection of DA methodologies within …
abstract amplify arxiv augmentation capacity classification cs.lg current data datasets however landscape model robustness overfitting robustness samples series strategy study survey time series training type
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