An Unsupervised Approach for Periodic Source Detection in Time Series

Berken Utku Demirel, Christian Holz
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:10409-10439, 2024.

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 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 augmentation. 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. We mitigate the collapse issue by ensuring the learned representations retain information from the original samples without imposing any 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-demirel24b, title = {An Unsupervised Approach for Periodic Source Detection in Time Series}, author = {Demirel, Berken Utku and Holz, Christian}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {10409--10439}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/demirel24b/demirel24b.pdf}, url = {https://proceedings.mlr.press/v235/demirel24b.html}, 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 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 augmentation. 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. We mitigate the collapse issue by ensuring the learned representations retain information from the original samples without imposing any 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.} }
Endnote
%0 Conference Paper %T An Unsupervised Approach for Periodic Source Detection in Time Series %A Berken Utku Demirel %A Christian Holz %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-demirel24b %I PMLR %P 10409--10439 %U https://proceedings.mlr.press/v235/demirel24b.html %V 235 %X 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 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 augmentation. 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. We mitigate the collapse issue by ensuring the learned representations retain information from the original samples without imposing any 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.
APA
Demirel, B.U. & Holz, C.. (2024). An Unsupervised Approach for Periodic Source Detection in Time Series. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:10409-10439 Available from https://proceedings.mlr.press/v235/demirel24b.html.

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