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Latent graph learning for multivariate time series

Dr Xiang Zhang



Multivariate time series are prevalent in a variety of domains, including healthcare, transportation, space science, biology, and finance. Recently, it has attracted increasing attention to leverage the structural relationships among channels to learn stronger representation. Previous studies demonstrated that inter-sensor correlations bring rich information in modelling time series. In this project, we will learn how to use a graph neural network model to learn the latent relationship between different time series sensors (such as different leads in ECG signals). The learned graph patterns will correspond to the task. The model will be evaluated by the PhysioNet benchmark.

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