PaddleTS is a Python library for deep time series modeling, built on the PaddlePaddle framework. It offers a comprehensive suite of tools for time series forecasting, representation learning, and anomaly detection, targeting domain experts and industry users seeking to leverage state-of-the-art deep learning models with an easy-to-use interface.

How It Works

PaddleTS provides a unified data structure for diverse time series data, supporting single/multi-target variables and covariates. It encapsulates common deep learning model functionalities like data loading, callbacks, and training control, allowing developers to focus on network architecture. The library integrates a wide array of leading deep learning models, data transformation operators, and classic data analysis tools, along with automatic hyperparameter optimization (AutoTS) and seamless integration with third-party libraries like scikit-learn and PyOD.

Quick Start & Requirements

• Installation: pip install paddlets
• Prerequisites: Python 3.7+, PaddlePaddle (GPU support recommended).
• Documentation: Get Started, API Docs
• Low-code Development: PaddleX offers a low-code interface for time series tasks.

Highlighted Details

• Supports 13 models via PaddleX for quick experimentation across forecasting, anomaly detection, and classification.
• Integrates 7 new forecasting algorithms (DLinear, PatchTST, TimesNet, etc.) and 5 anomaly detection algorithms.
• Offers ensemble learning capabilities for enhanced time series prediction.
• Includes modules for data analysis, feature engineering, and model interpretability (XAI).

Maintenance & Community

• Active development with frequent updates adding new models and features.
• Contributions are welcomed via GitHub issues and pull requests; prior discussion for significant changes is encouraged.
• Links to GitHub issues for bug reporting and feature discussion.

Licensing & Compatibility

• License: Apache-style license.
• Compatibility: Permissive license suitable for commercial use and integration with closed-source projects.

Limitations & Caveats

The library's primary dependency is PaddlePaddle, which may require specific hardware (e.g., NVIDIA GPUs) for optimal performance. While extensive, the rapid addition of new models might lead to occasional breaking changes or require users to stay updated with the latest documentation.