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chronos-bolt-tiny

Time Series Forecasting

pretrained models

foundation models

time series foundation models

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 # Chronos-Bolt⚡ (Tiny)
 Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the [T5 encoder-decoder architecture](https://arxiv.org/abs/1910.10683) and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are up to 250 times faster and 20 times more memory-efficient than the [original Chronos](https://arxiv.org/abs/2403.07815) models of the same size.
 ## Performance

 # Chronos-Bolt⚡ (Tiny)
+🚀 **Update Feb 14, 2025**: Chronos-Bolt models are now available on Amazon SageMaker JumpStart! Check out the [tutorial notebook](https://github.com/amazon-science/chronos-forecasting/blob/main/notebooks/deploy-chronos-bolt-to-amazon-sagemaker.ipynb) to learn how to deploy Chronos endpoints for production use in a few lines of code.
 Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the [T5 encoder-decoder architecture](https://arxiv.org/abs/1910.10683) and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are up to 250 times faster and 20 times more memory-efficient than the [original Chronos](https://arxiv.org/abs/2403.07815) models of the same size.
 ## Performance