2 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
Interpretable time series neural representation for classification purposes
Etienne Le Naour, Ghislain Agoua, Nicolas Baskiotis +1
Deep learning has made significant advances in creating efficient representations of time series data by automatically identifying complex patterns. However, these approaches lack…
Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
Etienne Le Naour, Louis Serrano, Léon Migus +5
We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular sample…