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stat.ML2026
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…
stat.ML2026
PAC-Bayesian Reconstruction Guarantees for Time Series Variational Autoencoders
Chloé Hashimoto-Cullen, Ghislain Agoua, Benjamin Guedj +1
Forecasting time series accurately is critical for applications with complex data ranging from energy systems to healthcare and finance. Among current state of the art models, gene…
cs.LG2026
Cross-Domain Offshore Wind Power Forecasting: Transfer Learning Through Meteorological Clusters
Dominic Weisser, Chloé Hashimoto-Cullen, Benjamin Guedj
Ambitious decarbonisation targets are rapidly increasing the commission of new offshore wind farms. For these newly commissioned plants to run, accurate power forecasts are needed…