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

TL;DR

Lag-Llama introduces a transformer-based foundation model for probabilistic time series forecasting, demonstrating strong zero-shot capabilities and state-of-the-art performance after fine-tuning across diverse domains.

Contribution

This work develops Lag-Llama, a novel foundation model for time series forecasting that leverages a decoder-only transformer architecture and large-scale pretraining.

Findings

Strong zero-shot generalization across domains

Outperforms existing models after fine-tuning

Sets new state-of-the-art performance in probabilistic forecasting

Abstract

Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization. However, despite the success of foundation models in modalities such as natural language processing and computer vision, the development of foundation models for time series forecasting has lagged behind. We present Lag-Llama, a general-purpose foundation model for univariate probabilistic time series forecasting based on a decoder-only transformer architecture that uses lags as covariates. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities compared to a wide range of forecasting models on downstream datasets across domains. Moreover, when fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance, outperforming prior deep learning approaches, emerging as the best general-purpose model on average. Lag-Llama serves as a strong contender to the current state-of-art in time series forecasting and paves the way for future advancements in foundation models tailored to time series data.