We investigate the effectiveness of time series foundation models (TSFMs) for crowd flow prediction, focusing on Moirai and TimesFM. Evaluated on three real-world mobility datasets-Bike NYC, Taxi Beijing, and Spanish national OD flows-these models are deployed in a strict zero-shot setting, using only the temporal evolution of each OD flow and no explicit spatial information. Moirai and TimesFM outperform both statistical and deep learning baselines, achieving up to 33% lower RMSE, 39% lower MAE and up to 49% higher CPC compared to state-of-the-art competitors. Our results highlight the practical value of TSFMs for accurate, scalable flow prediction, even in scenarios with limited annotated data or missing spatial context.
@article{arxiv.2507.00945,
title = {Time Series Foundation Models are Flow Predictors},
author = {Massimiliano Luca and Ciro Beneduce and Bruno Lepri},
journal= {arXiv preprint arXiv:2507.00945},
year = {2025}
}
Comments
arXiv admin note: text overlap with arXiv:2203.07372