Abstract
In this paper, we propose MotifAug, a parameter-free, pattern mixing-based time series data augmentation method that improves previous approaches in the literature. MotifAug leverages the warping path constructed by MotifDTW, a novel alignment method that uses the Matrix Profile (MP) motif discovery mechanism and Dynamic Time Warping (DTW) to align two time series data instances.
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Acknowledgments
This project has been supported in part by funding from GEO Directorate under NSF awards #2204363, #2240022, and #2301397 and the CISE Directorate under NSF award #2305781.
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Bahri, O., Li, P., Boubrahimi, S.F., Hamdi, S.M. (2023). Motif Alignment for Time Series Data Augmentation. In: Wrembel, R., Gamper, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2023. Lecture Notes in Computer Science, vol 14148. Springer, Cham. https://doi.org/10.1007/978-3-031-39831-5_4
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DOI: https://doi.org/10.1007/978-3-031-39831-5_4
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