The success of contrastive learning is well known to be dependent on data augmentation. Although the degree of data augmentations has been well controlled by utilizing pre-defined techniques in some domains like vision, time-series data augmentation is less explored and remains a challenging problem due to the complexity of the data generation mechanism, such as the intricate mechanism involved in the cardiovascular system. Moreover, there is no widely recognized and general time-series augmentation method that can be applied across different tasks. In this paper, we propose a novel data augmentation method for quasi-periodic time-series tasks that aims to connect intra-class samples together, and thereby find order in the latent space. Our method builds upon the well-known mixup technique by incorporating a novel approach that accounts for the periodic nature of non-stationary time-series. Also, by controlling the degree of chaos created by data augmentation, our method leads to improved feature representations and performance on downstream tasks. We evaluate our proposed method on three time-series tasks, including heart rate estimation, human activity recognition, and cardiovascular disease detection. Extensive experiments against state-of-the-art methods show that the proposed approach outperforms prior works on optimal data generation and known data augmentation techniques in the three tasks, reflecting the effectiveness of the presented method. Source code: https://github.com/eth-siplab/Finding_Order_in_Chaos

翻译：对比学习的成功众所周知依赖于数据增强。尽管在视觉等领域通过利用预定义技术已能良好控制数据增强程度，但时间序列的数据增强方法研究尚不充分，且因数据生成机制（如心血管系统的复杂机制）的复杂性而仍具挑战性。此外，目前尚无公认的通用时间序列增强方法可跨不同任务应用。本文针对准周期时间序列任务，提出一种旨在连接类内样本的新型数据增强方法，从而在潜在空间中寻找秩序。该方法基于广为人知的mixup技术，融入考虑非平稳时间序列周期特性的创新方案。同时，通过控制数据增强产生的混沌程度，该方法能够改善特征表示并提升下游任务性能。我们在心率估计、人类活动识别和心血管疾病检测三项时间序列任务上评估了所提方法。与最先进方法的大量实验表明，所提方法在这三项任务的最优数据生成与已知数据增强技术方面均优于先前工作，充分验证了所提方法的有效性。源代码：https://github.com/eth-siplab/Finding_Order_in_Chaos