amazon

Update Dec 30, 2025: ☁️ Deploy Chronos-2 on Amazon SageMaker. New guide covers real-time GPU and CPU inference, serverless endpoints (run on demand, no idle costs), and batch transform for large-sc...

--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series library_name: chronos-forecasting new_version: amazon/chronos-2 ---

--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series library_name: chronos-forecasting new_version: amazon/chronos-2 ---

--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series library_name: chronos-forecasting new_version: amazon/chronos-2 ---

--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series library_name: chronos-forecasting new_version: amazon/chronos-2 ---

--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series library_name: chronos-forecasting new_version: amazon/chronos-2 ---

--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series library_name: chronos-forecasting new_version: amazon/chronos-2 ---

🚀 Update Feb 14, 2025: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. 🚀 Update Nov 27, 2024: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes. For details on Chronos models, training data and procedures, and experimental results, please refer to the paper Chronos: Learning the Language of Time Series. Fig. 1: High-level depiction of Chronos. ( Left ) The input time series is scaled and quantized to obtain a sequence of tokens. ( Center ) The tokens are fed into a language model which may either be an encoder-decoder or a decoder-only model. The model is trained using the cross-entropy loss. ( Right ) During inference, we autoregressively sample tokens from the model and map them back to numerical values. Multiple trajectories are sampled to obtain a predictive distribution. The models in this repository are based on the T5 architecture. The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters. | Model | Parameters | Based on | | ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- | | chronos-t5-tiny | 8M | t5-efficient-tiny | | chronos-t5-mini | 20M | t5-efficient-mini | | chronos-t5-small | 46M | t5-efficient-small | | chronos-t5-base | 200M | t5-efficient-base | | chronos-t5-large | 710M | t5-efficient-large | To perform inference with Chronos models, install the package in the GitHub companion repo by running: A minimal example showing how to perform inference using Chronos models: If you find Chronos models useful for your research, please consider citing the associated paper: This project is licensed under the Apache-2.0 License.

🚀 Update Feb 14, 2025: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. 🚀 Update Nov 27, 2024: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes. For details on Chronos models, training data and procedures, and experimental results, please refer to the paper Chronos: Learning the Language of Time Series. Fig. 1: High-level depiction of Chronos. ( Left ) The input time series is scaled and quantized to obtain a sequence of tokens. ( Center ) The tokens are fed into a language model which may either be an encoder-decoder or a decoder-only model. The model is trained using the cross-entropy loss. ( Right ) During inference, we autoregressively sample tokens from the model and map them back to numerical values. Multiple trajectories are sampled to obtain a predictive distribution. The models in this repository are based on the T5 architecture. The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters. | Model | Parameters | Based on | | ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- | | chronos-t5-tiny | 8M | t5-efficient-tiny | | chronos-t5-mini | 20M | t5-efficient-mini | | chronos-t5-small | 46M | t5-efficient-small | | chronos-t5-base | 200M | t5-efficient-base | | chronos-t5-large | 710M | t5-efficient-large | To perform inference with Chronos models, install the package in the GitHub companion repo by running: A minimal example showing how to perform inference using Chronos models: If you find Chronos models useful for your research, please consider citing the associated paper: This project is licensed under the Apache-2.0 License.