PyPOTS

What it does

PyPOTS is the first (and so far the only) Python toolbox/library specifically designed for data mining and machine learning on partially-observed time series (POTS), namely, incomplete time series with missing values, A.K.A. irregularly-sampled time series.

PyPOTS: A comprehensive Python library for time series analysis with machine learning and deep learning. Offering 50+ state-of-the-art (SOTA) algorithms for time series imputation, forecasting, classification, anomaly detection, and clustering. Specifically designed for partially-observed time series (POTS) with missing values. Built on PyTorch with unified APIs, extensive documentation, and b

End-to-End Learning for Partially-Observed Time Series with PyPOTS KDD 2026 Hands-on Tutorial (System-Focused) 📍 Halla A, ICC, Jeju, Korea 🗓️ 1PM - 4PM, Aug 9, 2026 💻 Interactive Colab / Jupyter ⭐ GitHub BrewPOTS Repo 📖 PyPOTS Documentation 📌 Abstract & Overview Partially-observed time series (POTS) are ubiquitous in real-world applications (IoT sensors, healthcare vitals, industrial monitors, and financial records). However, conventional machine learning toolchains isolate missing-value imputation from downstream predictive modeling, causing error propagation, poor reusability, and fragmentedfrom pypots.com

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