autogluon

🚀 Update Feb 14, 2025: Chronos-Bolt 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. 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 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 more accurate, up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. The following plot compares the inference time of Chronos-Bolt against the original Chronos models for forecasting 1024 time series with a context length of 512 observations and a prediction horizon of 64 steps. Chronos-Bolt models are not only significantly faster but also more accurate than the original Chronos models. The following plot reports the probabilistic and point forecasting performance of Chronos-Bolt in terms of the Weighted Quantile Loss (WQL) and the Mean Absolute Scaled Error (MASE), respectively, aggregated over 27 datasets (see the Chronos paper for details on this benchmark). Remarkably, despite having no prior exposure to these datasets during training, the zero-shot Chronos-Bolt models outperform commonly used statistical models and deep learning models that have been trained on these datasets (highlighted by ). Furthermore, they also perform better than other FMs, denoted by a +, which indicates that these models were pretrained on certain datasets in our benchmark and are not entirely zero-shot. Notably, Chronos-Bolt (Base) also surpasses the original Chronos (Large) model in terms of the forecasting accuracy while being over 600 times faster. Chronos-Bolt models are available in the following sizes. | Model | Parameters | Based on | | ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- | | chronos-bolt-tiny | 9M | t5-efficient-tiny | | chronos-bolt-mini | 21M | t5-efficient-mini | | chronos-bolt-small | 48M | t5-efficient-small | | chronos-bolt-base | 205M | t5-efficient-base | For more advanced features such as fine-tuning and forecasting with covariates, check out this tutorial. Deploying a Chronos-Bolt endpoint to SageMaker First, update the SageMaker SDK to make sure that all the latest models are available. Now you can send time series data to the endpoint in JSON format. Chronos-Bolt models can be deployed to both CPU and GPU instances. These models also support forecasting with covariates. For more details about the endpoint API, check out the example notebook. If you find Chronos or Chronos-Bolt 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 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. 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 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 more accurate, up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. The following plot compares the inference time of Chronos-Bolt against the original Chronos models for forecasting 1024 time series with a context length of 512 observations and a prediction horizon of 64 steps. Chronos-Bolt models are not only significantly faster but also more accurate than the original Chronos models. The following plot reports the probabilistic and point forecasting performance of Chronos-Bolt in terms of the Weighted Quantile Loss (WQL) and the Mean Absolute Scaled Error (MASE), respectively, aggregated over 27 datasets (see the Chronos paper for details on this benchmark). Remarkably, despite having no prior exposure to these datasets during training, the zero-shot Chronos-Bolt models outperform commonly used statistical models and deep learning models that have been trained on these datasets (highlighted by ). Furthermore, they also perform better than other FMs, denoted by a +, which indicates that these models were pretrained on certain datasets in our benchmark and are not entirely zero-shot. Notably, Chronos-Bolt (Base) also surpasses the original Chronos (Large) model in terms of the forecasting accuracy while being over 600 times faster. Chronos-Bolt models are available in the following sizes. | Model | Parameters | Based on | | ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- | | chronos-bolt-tiny | 9M | t5-efficient-tiny | | chronos-bolt-mini | 21M | t5-efficient-mini | | chronos-bolt-small | 48M | t5-efficient-small | | chronos-bolt-base | 205M | t5-efficient-base | For more advanced features such as fine-tuning and forecasting with covariates, check out this tutorial. Deploying a Chronos-Bolt endpoint to SageMaker First, update the SageMaker SDK to make sure that all the latest models are available. Now you can send time series data to the endpoint in JSON format. Chronos-Bolt models can be deployed to both CPU and GPU instances. These models also support forecasting with covariates. For more details about the endpoint API, check out the example notebook. If you find Chronos or Chronos-Bolt models useful for your research, please consider citing the associated paper: This project is licensed under the Apache-2.0 License.

--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series ---

--- license: apache-2.0 pipeline_tag: time-series-forecasting tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series ---

--- license: apache-2.0 pipeline_tag: tabular-regression ---

🚀 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.

🚀 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.