4 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…
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…
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…
Predicting Electricity Consumption with Random Walks on Gaussian Processes
Chloé Hashimoto-Cullen, Benjamin Guedj
We consider time-series forecasting problems where data is scarce, difficult to gather, or induces a prohibitive computational cost. As a first attempt, we focus on short-term elec…