An Unsupervised Approach for Periodic Source Detection in Time Series

Berken Demirel, Christian Holz, Unsupervised Detection

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

Repositories linked to this paper

• eth-siplab/unsupervised_periodicity_detection — marked official in Papers with Code; named in the paper

Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.

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.

For agents

The same record, over MCP at https://syntology.ai/mcp:

get_harvested_code_for_paper("2406.00566")
get_code_for_paper("2406.00566")
have("2406.00566")

Connect an agent — have() is free.