Multiloss-Based Optimization for Time Series Data Augmentation

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

Abstract: Data augmentation plays an important part in the current success of machine learning and deep learning models. In particular, state-of-the-art architectures in the image recognition field include data augmentation modules as an integral part. However, there is still room for progress in the time series domain. In this work, we introduce OptimAug, a novel method for time series data augmentation. We deviate from the current state-of-the-art comprised of random transformations, pattern mixing, generative models, and decomposition methods, to develop the first multiloss-based optimization method. We evaluate our method with its two variants on datasets from the University of California Riverside (UCR) archive and compare it to multiple baseline algorithms from the literature.