Learning Embeddings for Completion and Prediction of Relationnal Multivariate Time-Series

We focus on learning over multivariate and relational timeseries where relations are modeled by a graph. We propose a model that is able to simultaneously fill in missing values and predict future ones. This approach is based on representation learning techniques, where temporal data are represented in a latent vector space so as to capture the dynamicity of the process and also the relations between the different sources. Information completion (missing values) and prediction are performed simultaneously using a unique formalism, whereas most often they are addressed separately using different methods.