This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series.
We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series.
This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs).
Empirically, we show that the convolution exponential outperforms other linear transformations in generative flows on CIFAR10 and the graph convolution exponential improves the performance of graph normalizing flows.
We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.