3 papers
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.LG2025
Are Time-Indexed Foundation Models the Future of Time Series Imputation?
Etienne Le Naour, Tahar Nabil, Adrien Petralia +1
Foundation models for time series imputation remain largely unexplored. Recently, two such models, TabPFN-TS and MoTM, have emerged. These models share a common philosophy that pla…
cs.LG2025
MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling
Etienne Le Naour, Tahar Nabil, Ghislain Agoua
Recent years have witnessed a growing interest for time series foundation models, with a strong emphasis on the forecasting task. Yet, the crucial task of out-of-domain imputation…