Motif Alignment for Time Series Data Augmentation

Omar Bahri, Peiyu Li, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi

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.