PyPOTS统一处理时间序列缺失数据，实现从修复到预测的端到端学习。
End-to-End Learning for Partially-Observed Time Series with PyPOTS
- 构建统一API，将缺失值处理与下游任务无缝衔接
- 支持插补、预测、分类等6类任务，覆盖完整分析流程
- 适合研究者和工程师快速搭建可复现的时序分析管道
真实世界中部分观测时间序列（POTS）极为常见，但现有工具链通常将缺失值处理与下游学习分离，影响可复现性与整体性能。本教程介绍PyPOTS——一个开源的Python生态，用于POTS的端到端数据挖掘与机器学习。涵盖缺失模拟、预处理、模型训练与评估，支持插补、预测、分类、聚类、异常检测等核心任务。第一部分面向实践者，提供统一接口与基准实验；第二部分面向开发者，聚焦自定义模型、领域约束扩展及可贡献工程规范。参与者将获得理论理解与实际开发经验，适用于科研与生产环境中的鲁棒、透明、可复用的POTS流水线构建。PyPOTS已开源：https://github.com/WenjieDu/PyPOTS。
原文摘要 · Abstract (English)
Partially-observed time series (POTS) is ubiquitous in real-world applications, yet most existing toolchains separate missing-value handling from downstream learning, which limits reproducibility and overall performance. This tutorial introduces PyPOTS, an open-source Python ecosystem for end-to-end data mining and machine learning on POTS. We present practical workflows spanning missingness simulation, data preprocessing, model training, and evaluation across core tasks, including imputation, forecasting, classification, clustering, and anomaly detection. The tutorial consists of two parts: Part I emphasizes hands-on application for practitioners through unified APIs and benchmark-oriented experiments. Part II targets developers and researchers, focusing on extending PyPOTS with custom models, domain-specific constraints, and contribution-ready engineering practices. Participants will gain both conceptual understanding and implementation experience for building robust, transparent, and reusable POTS pipelines in research and production settings. PyPOTS is publicly available at https://github.com/WenjieDu/PyPOTS
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