Downloadchronos-bolt-small is a compact, 48M-parameter model for zero-shot time series forecasting, developed by AutoGluon and based on the t5-efficient-small architecture. It leverages a patch-based encoder-decoder design to chunk historical data and generate direct multi-step quantile forecasts. Trained on nearly 100 billion time series observations, it delivers high accuracy while being up to 250× faster and 20× more memory-efficient than its predecessor, Chronos. The model excels at both probabilistic and point forecasting across diverse domains without prior exposure to target datasets. Benchmarking shows that even this small variant outperforms traditional statistical methods and many trained deep learning models. It is designed to be easily integrated into workflows using AutoGluon or deployed at scale on SageMaker. Chronos-Bolt is especially suitable for scalable forecasting tasks in production environments where speed, accuracy, and memory efficiency are critical.
Features
- Based on T5 encoder-decoder architecture for forecasting
- 48M parameters; optimized for speed and memory usage
- Supports zero-shot multi-step quantile forecasting
- Trained on 100B+ time series observations across domains
- Compatible with AutoGluon and Amazon SageMaker JumpStart
- Outperforms many trained models in both accuracy and speed
- Suitable for both CPU and GPU inference
- Accepts covariates and can be fine-tuned for specific use cases
Project Samples
