A Deep Neural Network for Multivariate Time Series Clustering with Result Interpretation

In today's industrial and scientific arenas, large quantities of multivariate time series data are generated without labels. Clustering such data is an important but challenging task due to complex variable associations. Unlike other previous efforts, this work explicitly explores variable associations through learning variable association graphs for each cluster. This is achieved through time series autoregression by a multi-path neural network, where each path corresponds to one cluster. The learned variable association graphs can be used to interpret how one cluster differs from another. Experiments demonstrate our framework's effectiveness on clustering and result interpretability.