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PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. A base model class that provides basic training of time series models along with logging in tensorboard and generic visualizations such actual vs predictions and dependency plots. Multiple neural network architectures for timeseries forecasting that have been enhanced for real-world deployment and come with in-built interpretation capabilities. The package is built on PyTorch Lightning to allow training on CPUs, single and multiple GPUs out-of-the-box.
Features
- Multi-horizon timeseries metrics
- Ranger optimizer for faster model training
- Hyperparameter tuning with optuna
- A base model class which provides basic training of timeseries models
- Multiple neural network architectures
- A timeseries dataset class which abstracts handling variable transformations
Project Samples
