eth-siplab/Unsupervised_Periodicity_Detection
Official code for ICML 2024 paper "An Unsupervised Approach for Periodic Source Detection in Time Series"
This helps researchers and analysts automatically identify repeating patterns within noisy time series data, like heart rate or respiration, without needing pre-labeled examples or carefully crafted data. It takes raw, unlabeled time series data as input and highlights the underlying periodic components. This is ideal for scientists, medical researchers, or behavioral analysts working with physiological or activity sensor data.
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Use this if you need to find recurring cycles in time series data without the burden of manually labeling data or struggling with complex data augmentation techniques.
Not ideal if your data is not time series, or if you already have perfectly labeled data and prefer traditional supervised learning methods.
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Language
Python
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Last pushed
Feb 21, 2025
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