Downloadchronos-t5-small is part of Amazon’s Chronos family of time series forecasting models built on transformer-based language model architectures. It repurposes the T5 encoder-decoder design for time series data by transforming time series into discrete tokens via scaling and quantization. With 46 million parameters and a reduced vocabulary of 4096 tokens, this small variant balances performance with efficiency. Trained on both real-world and synthetic time series datasets, it supports probabilistic forecasting by autoregressively sampling multiple future trajectories. The model is capable of generating full predictive distributions, making it well-suited for uncertainty-aware forecasting. It is compatible with the Chronos Python package and integrates easily into forecasting pipelines using PyTorch. Chronos models are open-source under Apache 2.0 and have been demonstrated to perform competitively in forecasting benchmarks.
Features
- Based on T5 architecture with 46M parameters
- Uses 4096-token vocabulary tailored for time series
- Supports probabilistic, autoregressive forecasting
- Compatible with PyTorch and ChronosPipeline
- Trained on real and synthetic datasets
- Lightweight and efficient for small-scale deployment
- Easily visualized with integrated forecast intervals
- Apache 2.0 licensed and openly available
Project Samples
