Lag Llama

Lag-llama is a probabilistic machine learning foundation model designed for time series forecasting. It generates predictive distributions and uncertainty bounds for sequential data across arbitrary frequencies by leveraging pre-trained foundational weights.

The system supports zero-shot transfer inference, allowing it to predict future values on entirely new and unseen datasets without requiring prior retraining. It achieves this by combining generalized representations from foundational training with adjustable context lengths, where historical context lengths and lagged feature values feed directly into the model inputs during iterative autoregressive predictions.

Additionally, the framework provides capabilities for foundation model fine-tuning. Users can adapt pre-trained weights and adjust forecasting models on specialized datasets using targeted hyperparameters and validation splits to optimize domain-specific predictive accuracy.

Features

Zero-Shot Forecasting - Predicts future values on new datasets without prior training by leveraging pre-trained foundational knowledge and adjustable context lengths.
Autoregressive Lag Tuning - Predicts future values iteratively by feeding historical context lengths and lagged feature values directly into model inputs.
Probabilistic Forecasting - Generates predictive distributions and uncertainty bounds for sequential data across arbitrary frequencies using probabilistic deep learning.
Time Series Foundation Models - Serves as a probabilistic machine learning model pretrained on sequential data to forecast future values and quantify uncertainty across arbitrary frequencies.
Finetuning Workflows - Adjusts foundational weights on custom datasets using dedicated hyperparameters and validation splits to maximize prediction accuracy.
Zero-Shot Inference - Predicts future values on unseen target datasets without prior retraining by leveraging generalized representations from foundational training.
Probabilistic Hybrid Models - Generates predictive distributions and uncertainty bounds for sequential data across arbitrary frequencies using probabilistic modeling.
Foundational Model Adaptation - Adapts pre-trained neural network weights to specialized downstream domains using targeted hyperparameter tuning and validation splits.
Time Series Forecasting Fine-Tuners - Tunes pre-trained time series models on specialized datasets to improve predictive accuracy for domain-specific workloads.
Probability Distributions - Produces probability distributions for future timesteps to quantify uncertainty across any prediction length and data frequency.

Star history

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time-series-foundation-modelslag-llama

Name: time-series-foundation-models/lag-llama
Author: time-series-foundation-models

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Lag Llama

Features

Zero-Shot Forecasting - Predicts future values on new datasets without prior training by leveraging pre-trained foundational knowledge and adjustable context lengths.
Autoregressive Lag Tuning - Predicts future values iteratively by feeding historical context lengths and lagged feature values directly into model inputs.
Probabilistic Forecasting - Generates predictive distributions and uncertainty bounds for sequential data across arbitrary frequencies using probabilistic deep learning.
Time Series Foundation Models - Serves as a probabilistic machine learning model pretrained on sequential data to forecast future values and quantify uncertainty across arbitrary frequencies.
Finetuning Workflows - Adjusts foundational weights on custom datasets using dedicated hyperparameters and validation splits to maximize prediction accuracy.
Zero-Shot Inference - Predicts future values on unseen target datasets without prior retraining by leveraging generalized representations from foundational training.
Probabilistic Hybrid Models - Generates predictive distributions and uncertainty bounds for sequential data across arbitrary frequencies using probabilistic modeling.
Foundational Model Adaptation - Adapts pre-trained neural network weights to specialized downstream domains using targeted hyperparameter tuning and validation splits.
Time Series Forecasting Fine-Tuners - Tunes pre-trained time series models on specialized datasets to improve predictive accuracy for domain-specific workloads.
Probability Distributions - Produces probability distributions for future timesteps to quantify uncertainty across any prediction length and data frequency.

Projects sharing features with Lag Llama

These projects share indexed features with Lag Llama. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.

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Frequently asked questions

What does time-series-foundation-models/lag-llama do?

What are the main features of time-series-foundation-models/lag-llama?

The main features of time-series-foundation-models/lag-llama are: Zero-Shot Forecasting, Autoregressive Lag Tuning, Probabilistic Forecasting, Time Series Foundation Models, Finetuning Workflows, Zero-Shot Inference, Probabilistic Hybrid Models, Foundational Model Adaptation.

Which projects share features with time-series-foundation-models/lag-llama?

Projects with overlapping indexed features include: autogluon/autogluon — AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end… amazon-science/chronos-forecasting — Chronos-forecasting is a zero-shot time series forecasting framework based on a pretrained large language model. It… awslabs/gluon-ts — GluonTS is a framework for probabilistic time series forecasting, designed to predict future values as probability… awslabs/gluonts — GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for… thuml/time-series-library — This PyTorch-based deep learning library provides a framework for analyzing and forecasting temporal data. It… unit8co/darts — Darts is a Python time series library designed for forecasting, anomaly detection, and the preprocessing of univariate…