155 citations · 356 across the 34 of their papers we have counts for
37 papers
Latent Inference-Time Guidance of Time Series Foundation Models
Chloé Hashimoto-Cullen, Amaury Durand, Laurent Bozzi +3
Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on in-context learning to make the…
GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting
Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +1
Electricity forecasting often involves spatially related signals observed over regions, substations, and feeders, and Graph Neural Networks (GNNs) provide a natural way to represen…
Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation
Yutong Wang, Yannig Goude, Qiwei Yao
We study online prediction under distribution shift, where inputs arrive chronologically and outcomes are revealed only after prediction. In this setting, predictors must remain st…
Spatio-temporal modelling of electric vehicle charging demand
Kaoutar Bouaachra, Yvenn Amara-Ouali, Yannig Goude +1
Accurate forecasting of electric vehicle (EV) charging demand is critical for grid management and infrastructure planning. Yet the field continues to rely on legacy benchmarks; suc…
Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields
Julie Keisler, Boutheina Oueslati, Anastase Charantonis +2
General Circulation Models (GCMs) are widely used for future climate projections, but their coarse spatial resolution and systematic biases limit their direct use for impact studie…
SerpentFlow: Generative Unpaired Domain Alignment via Shared-Structure Decomposition
Julie Keisler, Anastase Alexandre Charantonis, Yannig Goude +2
Domain alignment refers broadly to learning correspondences between data distributions from distinct domains. In this work, we focus on a setting where domains share underlying str…