A Survey of Time Series Data Augmentation Techniques
DOI:
https://doi.org/10.29304/jqcsm.2026.18.32697Keywords:
data augmentationAbstract
Machine learning modeling require for a large amount of training data. Where more training data example results more robust models, but it is not training data can be available in suitable quantity. Also, in some situations, the quality of data that have rich information is leak. So to deal with these problem, augmentation is one of important and common solutions. Augmentation is a method for creating new artificial examples from the available data examples and added to data set. According to its importance, different methods have been proposed to perform augmentation. There methods vary according to the data types. Time series is one of data types that require more for augmentation because this type of data is not available in the suitable quantity or quality. Different time series data augmentation methods have been suggested, from traditional methods that use statistics tool, until the methods based deep learning. This paper would be present a review of these methods.
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Copyright (c) 2026 Muntaha Abdel Hamza Hamid1, Lamia Abed Noor Muhammed2
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
