WenjieDu/PyPOTS

A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values

What it solves

PyPOTS 解决了分析包含缺失值的真实世界时间序列数据（部分观测时间序列，简称 POTS）的挑战。传感器故障或通信错误导致的缺失数据常常阻碍高级数据分析和机器学习，而迄今为止，领域内缺乏专门且统一的工具套件来满足这些特定需求。

How it works

PyPOTS 提供一个全面的 Python 工具箱，整合了大量经典和最先进的机器学习算法，专门针对带缺失值的多变量时间序列进行适配。它提供统一的 API 和详细的文档，以简化这些模型的实现。对于原本未为 POTS 设计的模型，库会应用特定的嵌入策略和训练方法（如 ORT+MIT），使其能够兼容缺失数据。

Who it’s for

它面向需要处理时间序列缺失问题的研究人员和工程师，让他们无需在繁琐的数据预处理或手动实现算法上花费过多时间。

Highlights

  • Diverse Task Support: Supports imputation, forecasting, classification, clustering, and anomaly detection.
  • Extensive Algorithm Library: Includes a vast range of models from naive methods (mean/median) to advanced Neural Networks, Time-Series Foundation Models (TSFM), and Large Language Models (LLM) like GPT4TS.
  • Hyperparameter Optimization: Integrated support for Optuna and Microsoft NNI for tuning neural network models.
  • Ecosystem Integration: Works alongside TSDB (for easy dataset loading) and PyGrinder (for simulating missing data patterns like MCAR, MAR, and MNAR).