time-series-foundation-models/lag-llama

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1600 stars · 202 forks observed · 2026-10-01

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Lag-Llama is the first open-source foundation model for probabilistic time series forecasting, built on a transformer architecture. It provides pretrained model weights for zero-shot forecasting as well as scripts for pretraining and finetuning.

Use cases

• forecast future values of a time series zero-shot
• generate probabilistic forecasts with uncertainty estimates
• finetune a foundation model on my own time series data
• benchmark time series forecasting models
• replicate experiments from the Lag-Llama paper

When to choose

• you need probabilistic forecasts without training a model from scratch
• you want a pretrained zero-shot time series model in Python
• you want to finetune or study a time series foundation model

When to avoid

• you need classical statistical forecasting like ARIMA or Prophet
• you need a lightweight model for edge or low-resource deployment
• you need multivariate forecasting with rich covariates support

Facets

library · maturity active

machine-learning deep-learning transformers machine-learning time-series data-science python time-series-forecasting foundation-model probabilistic-forecasting zero-shot-forecasting lag-llama

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