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Maintainer: amazon

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Last updated 5/16/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The chronos-t5-large model is a time series forecasting model from Amazon that is based on the T5 architecture. Like other Chronos models, it transforms time series data into sequences of tokens using scaling and quantization, and then trains a language model on these tokens to learn patterns and generate future forecasts. The chronos-t5-large model has 710M parameters, making it the largest in the Chronos family, which also includes smaller variants like chronos-t5-tiny, chronos-t5-mini, and chronos-t5-base.

Chronos models are similar to other text-to-text transformer models like CodeT5-large and the original T5-large in their use of a unified text-to-text format and encoder-decoder architecture. However, Chronos is specifically designed and trained for time series forecasting tasks, while CodeT5 and T5 are more general-purpose language models.

Model inputs and outputs


  • Time series data: The Chronos-T5 models accept sequences of numerical time series values as input, which are then transformed into token sequences for modeling.


  • Probabilistic forecasts: The models generate future trajectories of the time series by autoregressively sampling tokens from the trained language model. This results in a predictive distribution over future values rather than a single point forecast.


The chronos-t5-large model and other Chronos variants have demonstrated strong performance on a variety of time series forecasting tasks, including datasets covering domains like finance, energy, and weather. By leveraging the large-scale T5 architecture, the models are able to capture complex patterns in the training data and generalize well to new time series. Additionally, the probabilistic nature of the outputs allows the models to capture uncertainty, which can be valuable in real-world forecasting applications.

What can I use it for?

The chronos-t5-large model and other Chronos variants can be used for a wide range of time series forecasting use cases, such as:

  • Financial forecasting: Predicting stock prices, exchange rates, or other financial time series
  • Energy demand forecasting: Forecasting electricity or fuel consumption for grid operators or energy companies
  • Demand planning: Forecasting product demand to optimize inventory and supply chain management
  • Weather and climate forecasting: Predicting weather patterns, temperature, precipitation, and other climate-related variables

To use the Chronos models, you can follow the example provided in the companion repository, which demonstrates how to load the model, preprocess your data, and generate forecasts.

Things to try

One key capability of the Chronos models is their ability to handle a wide range of time series data, from financial metrics to weather measurements. Try experimenting with different types of time series data to see how the model performs. You can also explore the impact of different preprocessing steps, such as scaling, quantization, and time series transformation, on the model's forecasting accuracy.

Another interesting aspect of the Chronos models is their probabilistic nature, which allows them to capture uncertainty in their forecasts. Try analyzing the predicted probability distributions and how they change based on the input data or model configuration. This information can be valuable for decision-making in real-world applications.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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