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

Ali Ziat, Gabriella Contardo, Nicolas Baskiotis, Ludovic Denoyer
2016 The European Symposium on Artificial Neural Networks  
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
more » ... ltaneously using a unique formalism, whereas most often they are addressed separately using different methods.
dblp:conf/esann/ZiatCBD16 fatcat:laf5uqdc3nhlnhzkvu45vgwfei