  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
