Home Projects lag-llama
lag-llama
Python

lag-llama

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

by time-series-foundation-models · GitHub
Stars
Forks
License
Created
Last commit
Category
Language
Likely
Self-hostable
forecastingfoundation-modelslag-llamaApache-2.0PythonSelf-hostable
View on GitHub
In plain words

Use an open-source model to predict future trends based on time series data.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
lag-llama — GitHub preview card
📈 Star history
1.60k1.59k
2026-07-202026-08-31
📈 Track lag-llama

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

lag-llama has 1.6k stars on GitHub. It has been forked 201 times. lag-llama is written mainly in Python. It has been in active development since 2024. lag-llama is available under the Apache-2.0 license. Its main topics are forecasting, foundation-models, lag-llama, llama.

Frequently asked questions

What is lag-llama?

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Is lag-llama open source?

lag-llama is an open-source project. It is released under the Apache-2.0 license.

Is lag-llama free?

Yes. lag-llama is free and open source — you can use, modify and self-host it.

What license does lag-llama use?

lag-llama is available under the Apache-2.0 license.

What language is lag-llama written in?

lag-llama is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — lag-llama

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=time-series-foundation-models-lag-llama)](https://olud.ai/project/time-series-foundation-models-lag-llama.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.