--- license: apache-2.0 tags: - time-series - time-series-forecasting - chronos - gluonts - transformer - pytorch datasets: - Monash-University/monash_tsf --- # GluonTS-Chronosformer This is a `louijiec/gluonts-chronosformer` model, which is a version of the Chronos model adapted for use with the GluonTS library. [1] This model leverages a transformer-based architecture to perform time series forecasting. Chronos is a family of pretrained time series models based on language model architectures. [12, 14] It has been shown to provide accurate zero-shot forecasts, often matching or exceeding the performance of models trained specifically for a particular dataset. [14] ## Model Description The Chronosformer, like the original Chronos model, is built upon a transformer architecture. [12] However, instead of processing text, it's designed to process sequences of time series data. The model is pretrained on a large corpus of time series data, enabling it to learn general patterns and trends that can be applied to new, unseen datasets. [12] This particular implementation is designed to be seamlessly integrated with GluonTS, a popular Python library for probabilistic time series modeling. [3] This allows you to leverage the powerful data loading, transformation, and evaluation tools provided by GluonTS when working with the Chronosformer model. ## Intended Uses & Limitations You can use the `louijiec/gluonts-chronosformer` for various time series forecasting tasks, such as: * **Zero-shot forecasting:** The model can generate forecasts for new time series without any additional training. This is useful for quickly getting a baseline forecast or for applications where you don't have enough data to train a new model from scratch. * **Fine-tuning:** You can fine-tune the model on your own dataset to improve its performance on a specific task. This is recommended if you have a large amount of data and want to achieve the best possible accuracy. However, there are some limitations to be aware of: * **Performance:** While Chronos has shown strong performance in many cases, it may not always be the best model for every dataset. It's always a good idea to compare its performance to other models, including classical statistical models and other deep learning models, to see what works best for your specific use case. [14] * **Computational resources:** The Chronosformer is a large model and may require significant computational resources to run, especially for training and fine-tuning. A GPU is recommended for optimal performance. [14] ## How to Get Started with an Example Here's a basic example of how to use the `louijiec/gluonts-chronosformer` model with GluonTS: #Coming soon