An Unsupervised Approach for Periodic Source Detection in Time Series

TL;DR

This paper introduces an unsupervised method for detecting periodic patterns in noisy time series data that outperforms existing techniques without relying on labels or complex augmentations.

Contribution

The authors propose a novel unsupervised approach that avoids representation collapse and does not require data augmentation, improving periodicity detection in time series.

Findings

Outperforms state-of-the-art methods by 45-50% in accuracy

Effective in multiple time series tasks without labeled data

Mitigates collapse issue in self-supervised learning for time series

Abstract

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: https://github.com/eth-siplab/Unsupervised_Periodicity_Detection