Overview
GluonTS is a Python toolkit dedicated to probabilistic time series modeling, with a strong emphasis on deep learning-based approaches. It provides a comprehensive suite of tools for time series data manipulation, model building, training, and evaluation. The library supports various forecasting models, including DeepAR, DeepState, and Transformer-based models. GluonTS uses PyTorch and MXNet as backend frameworks, offering flexibility in model implementation and customization. Key features include pre-built datasets, data splitting utilities, synthetic data generation, and customizable loss functions. GluonTS facilitates the development of custom models through PyTorch, tuning with Optuna, and using trainer callbacks. The core value lies in providing a unified platform for developing and deploying advanced time series forecasting solutions.