  Self-supervised contrastive learning has become a key technique in deep
learning, particularly in time series analysis, due to its ability to learn
meaningful representations without explicit supervision. Augmentation is a
critical component in contrastive learning, where different augmentations can
dramatically impact performance, sometimes influencing accuracy by over 30%.
However, the selection of augmentations is predominantly empirical which can be
suboptimal, or grid searching that is time-consuming. In this paper, we
establish a principled framework for selecting augmentations based on dataset
characteristics such as trend and seasonality. Specifically, we construct 12
synthetic datasets incorporating trend, seasonality, and integration weights.
We then evaluate the effectiveness of 8 different augmentations across these
synthetic datasets, thereby inducing generalizable associations between time
series characteristics and augmentation efficiency. Additionally, we evaluated
the induced associations across 6 real-world datasets encompassing domains such
as activity recognition, disease diagnosis, traffic monitoring, electricity
usage, mechanical fault prognosis, and finance. These real-world datasets are
diverse, covering a range from 1 to 12 channels, 2 to 10 classes, sequence
lengths of 14 to 1280, and data frequencies from 250 Hz to daily intervals. The
experimental results show that our proposed trend-seasonality-based
augmentation recommendation algorithm can accurately identify the effective
augmentations for a given time series dataset, achieving an average Recall@3 of
0.667, outperforming baselines. Our work provides guidance for studies
employing contrastive learning in time series analysis, with wide-ranging
applications. All the code, datasets, and analysis results will be released at
https://github.com/DL4mHealth/TS-Contrastive-Augmentation-Recommendation.
