@inproceedings{
rasul2023lagllama,
title={Lag-Llama: Towards Foundation Models for Time Series Forecasting},
author={Kashif Rasul and Arjun Ashok and Andrew Robert Williams and Arian Khorasani and George Adamopoulos and Rishika Bhagwatkar and Marin Bilo{\v{s}} and Hena Ghonia and Nadhir Hassen and Anderson Schneider and Sahil Garg and Alexandre Drouin and Nicolas Chapados and Yuriy Nevmyvaka and Irina Rish},
booktitle={R0-FoMo:Robustness of Few-shot and Zero-shot Learning in Large Foundation Models},
year={2023},
url={https://openreview.net/forum?id=jYluzCLFDM}
}
Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-Llama, a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen "out-of-distribution" time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws to fit and predict model scaling behavior. The open source code is made available at
https://github.com/kashif/pytorch-transformer-ts.
Lag-Llama: Towards Foundation Models for Time Series Forecasting
Kashif Rasul Arjun Ashok Andrew Robert Williams Arian Khorasani George Adamopoulos Rishika Bhagwatkar Marin Biloš Hena Ghonia Nadhir Hassen Anderson Schneider Sahil Garg Alexandre Drouin Nicolas Chapados Yuriy Nevmyvaka Irina Rish