pytorch-forecasting is a production-grade time series forecasting library built on PyTorch Lightning. It provides a comprehensive suite of state-of-the-art deep learning models including Temporal Fusion Transformer, N-BEATS, DeepAR, PatchTST, TiDE, and many more. The library handles the full pipeline: data loading and preprocessing (TimeSeriesDataSet), model definition with automatic parameter inference from data, training via Lightning's Trainer, and prediction with uncertainty quantification. It supports point forecasts, quantiles, and full distributional outputs. The codebase is highly modular with separate layers for attention, embeddings, normalization, and recurrent cells, enabling custom model building. A unique 'package' (v2) API offers a higher-level interface for rapid experimentation, though it's still experimental. The library is well-tested, includes extension templates for contributions, and integrates hyperparameter tuning via Optuna. However, the coexistence of stable v1 and experimental v2 APIs creates fragmentation, and some parts are in active redesign.
graph_weather is a research-oriented deep learning library for weather forecasting with graph neural networks. It implements multiple model families—GraphCast-inspired encoder/processor/decoder models, the diffusion-based GenCast and GenDA, the ensemble FGN, WeatherMesh, Aurora, FengWu-GHR, and CaFA—along with a substantial set of data loaders, loss functions, training scripts, and evaluation utilities. The package is structured as a Python library with HuggingFace Hub model integration, PyTorch Lightning training support, and a comprehensive test suite, giving it a hybrid character: it is fundamentally research code, but packaged and tested with application-grade discipline. Entry points include GraphWeatherForecaster, GraphWeatherAssimilator, WeatherStationReader, and various dataset classes, while training scripts live under train/.