Detection of periodic patterns of interest within noisy time series data
plays a critical role in various tasks, spanning from health monitoring to
behavior analysis. Existing learning techniques often rely on labels or clean
versions of signals for detecting the periodicity, and those employing
self-supervised learning methods are required to apply proper augmentations,
which is already challenging for time series and can result in collapse -- all
representations collapse to a single point due to strong augmentations. In this
work, we propose a novel method to detect the periodicity in time series
without the need for any labels or requiring tailored positive or negative data
generation mechanisms with specific augmentations. We mitigate the collapse
issue by ensuring the learned representations retain information from the
original samples without imposing any random variance constraints on the batch.
Our experiments in three time series tasks against state-of-the-art learning
methods show that the proposed approach consistently outperforms prior works,
achieving performance improvements of more than 45--50\%, showing its
effectiveness. Code:
this https URL

利用无标签数据和无需自定义数据生成机制的新方法检测时间序列数据中的周期性，克服了强数据增强可能导致的表示崩溃问题，并在三个时间序列任务中相对于现有学习方法表现出 45-50% 的性能提升。