Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations

1 code implementation • 9 Jun 2023 • Etienne Le Naour, Louis Serrano, Léon Migus, Yuan Yin, Ghislain Agoua, Nicolas Baskiotis, Patrick Gallinari, Vincent Guigue

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 samples, missing data, or unaligned measurements from multiple sensors.

Imputation Meta-Learning +1

Multi-scale Physical Representations for Approximating PDE Solutions with Graph Neural Operators

1 code implementation • 29 Jun 2022 • Léon Migus, Yuan Yin, Jocelyn Ahmed Mazari, Patrick Gallinari

In this work, we study three multi-resolution schema with integral kernel operators that can be approximated with \emph{Message Passing Graph Neural Networks} (MPGNNs).