Institut-Polytechnique-de-Paris/time-disentanglement-lib
🤗 [ICLR 2024] Disentangling Time Series Representations via Contrastive based l-Variational Inference
This library helps machine learning researchers break down complex time series data into simpler, independent components. You input sequential data, and it outputs a clearer, more organized representation that reveals distinct underlying factors. Researchers focusing on advanced time series analysis will find this useful for understanding complex patterns.
Use this if you are a machine learning researcher working with time series data and need to decompose it into independent, interpretable factors.
Not ideal if you are looking for a plug-and-play solution for business forecasting or general time series analysis without deep research into representation learning.
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Python
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Last pushed
Dec 11, 2025
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