Title: Toward Robust Feature Space in Long-Tailed Time Series Classification: A Multi-Scale Perspective
Primary area: learning on time series and dynamical systems
Keywords: Time series classification, Long-tailed recognition, Contrastive learning

Abstract: In recent years, significant progress has been made in time-series classification (TSC) research on balanced datasets.  However, research on long-tailed time-series classification tasks remains limited. In this paper, we propose a novel framework, TimeLT, designed to learn a robust feature space from long-tailed data, thereby improving the overall accuracy of long-tailed TSC, with a particular focus on tail classes.  Specifically, we introduce a perturbation-aware data augmentation strategy that increases the diversity of tail-class samples and enhances the model's robustness to extremely imbalanced data distributions.  Following this, we leverage a multi-scale temporal encoder to extract rich representations from time-series data. This multi-scale modeling approach facilitates the learning of unique features for each class. Furthermore, we propose a boundary-repulsion regularization term that encourages embeddings to move closer to their respective class centroids while being repelled from inter-class decision boundaries. This approach helps to form compact and discriminative feature spaces. To promote comprehensive research in this area, we consolidate a dedicated benchmark comprising several long-tailed datasets and over 14 advanced baselines. Extensive experiments conducted on this benchmark demonstrate that TimeLT significantly outperforms other advanced models, achieving performance improvements of up to \textbf{49.4\%} in highly imbalanced scenarios.