Welcome to the guide on HuggingFace Lag-Llama, the first open-source foundation model for time series forecasting! Below, you will find all the necessary information and resources to get started with Lag-Llama.

First and foremost, you can access Lag-Llama on HuggingFace at the following link: https://huggingface.co/time-series-foundation-models/Lag-Llama. Additionally, you can find the GitHub repository for Lag-Llama at https://github.com/time-series-foundation-models/lag-llama.

If you’re interested in learning more about Lag-Llama, you can read the paper at https://time-series-foundation-models.github.io/lag-llama.pdf. Please note that the latest version of the paper is available at this link, as the previous version on arXiv is outdated.

For a quick demonstration of Lag-Llama’s capabilities, you can check out the Colab Demo at https://colab.research.google.com/drive/13HHKYL_HflHBKxDWycXgIUAHSeHRR5eo?usp=sharing.

Current Features of Lag-Llama:
– Zero-shot forecasting on a dataset of any frequency for any prediction length, using the Colab Demo.

Coming Soon:
– An online gradio demo where you can upload time series and get zero-shot predictions.
– Features for finetuning the foundation model.
– Features for pretraining Lag-Llama on your own large-scale data.
– Scripts to reproduce all results in the paper.

Stay tuned for these upcoming features!

For those interested in accessing the pretrained checkpoint of Lag-Llama, you can find it at https://huggingface.co/time-series-foundation-models/Lag-Llama/blob/main/lag-llama.ckpt.

We hope this guide helps you get started with Lag-Llama and we look forward to the incredible applications and insights you’ll uncover using this powerful foundation model! 🦙

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# HuggingFace Lag-Llama Time Series Foundation Model Manual

Lag-Llama is the first open-source foundation model for time series forecasting. In this tutorial, we will walk you through the features and resources available for the Lag-Llama model on HuggingFace.

## Resources
– [Tweet Thread](https://twitter.com/arjunashok37/status/1755261111233114165)
– [HuggingFace Lag-Llama Model](https://huggingface.co/time-series-foundation-models/Lag-Llama)
– [Colab Demo](https://colab.research.google.com/drive/13HHKYL_HflHBKxDWycXgIUAHSeHRR5eo?usp=sharing)
– [GitHub Repository](https://github.com/time-series-foundation-models/lag-llama)
– [Lag-Llama Paper](https://time-series-foundation-models.github.io/lag-llama.pdf)

## Features
– **Zero-shot forecasting**: Forecast on a dataset of any frequency for any prediction length using the Colab Demo.
– Pretrained checkpoint available [here](https://huggingface.co/time-series-foundation-models/Lag-Llama/blob/main/lag-llama.ckpt)

## Coming Soon
– Online gradio demo for uploading time series and getting zero-shot predictions.
– Features for finetuning the foundation model.
– Features for pretraining Lag-Llama on your own large-scale data.
– Scripts to reproduce all results in the paper.

## Stay Tuned!
More features and resources are in development. Keep an eye on the HuggingFace Lag-Llama model for future updates.

For any inquiries or support, please refer to the HuggingFace Lag-Llama model page or the GitHub repository.

Thank you for choosing Lag-Llama for your time series forecasting needs! 🦙

lag-llama-architecture

Lag-Llama is the first open-source foundation model for time series forecasting!

Tweet Thread: https://twitter.com/arjunashok37/status/1755261111233114165

HuggingFace: https://huggingface.co/time-series-foundation-models/Lag-Llama

Colab Demo: https://colab.research.google.com/drive/13HHKYL_HflHBKxDWycXgIUAHSeHRR5eo?usp=sharing

GitHub: https://github.com/time-series-foundation-models/lag-llama

Paper: https://time-series-foundation-models.github.io/lag-llama.pdf

arXiv has a previous outdated version of the paper and is still being updated with the latest version; please use the above link to access the latest version.


This HuggingFace model houses the pretrained checkpoint of Lag-Llama.

Current Features:

💫 Zero-shot forecasting on a dataset of any frequency for any prediction length, using the Colab Demo.


Coming Soon:

⭐ An online gradio demo where you can upload time series and get zero-shot predictions.

⭐ Features for finetuning the foundation model

⭐ Features for pretraining Lag-Llama on your own large-scale data

⭐ Scripts to reproduce all results in the paper.


Stay Tuned!🦙


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