time-series-foundation-models/lag-llama

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

49
/ 100
Emerging

Lag-Llama helps businesses and researchers predict future values from historical data, like sales, stock prices, or sensor readings. It takes your existing time-series data and outputs a probabilistic forecast, showing not just a single prediction, but a range of possible outcomes. This tool is for data scientists, analysts, or operations managers who need to make informed decisions based on future trends.

1,556 stars. No commits in the last 6 months.

Use this if you need to forecast how a specific metric will change over time, especially if you have a wide variety of time series datasets and want a robust, general-purpose forecasting model.

Not ideal if you only need simple, point forecasts or if you prefer a model that gives a single, deterministic prediction without uncertainty estimates.

predictive-analytics demand-forecasting financial-modeling operations-planning data-analysis
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 21 / 25

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Stars

1,556

Forks

197

Language

Python

License

Apache-2.0

Last pushed

Jun 06, 2025

Commits (30d)

0

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