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This paper introduces a new approach for Multivariate Time Series forecastingthat jointly infers and leverages relations among time series. Its modularityallows it to be integrated with current univariate methods. Our approach allowsto trade-off accuracy and computational efficiency gradually via offering onone extreme inference of a potentially fully-connected graph or on anotherextreme a bipartite graph. In the potentially fully-connected case we considerall pair-wise interactions among time-series which yields the best forecastingaccuracy. Conversely, the bipartite case leverages the dependency structure byinter-communicating the N time series through a small set of K auxiliary nodesthat we introduce. This reduces the time and memory complexity w.r.t. previousgraph inference methods from O(N^2) to O(NK) with a small trade-off inaccuracy. We demonstrate the effectiveness of our model in a variety ofdatasets where both of its variants perform better or very competitively toprevious graph inference methods in terms of forecasting accuracy and timeefficiency.

Multivariate Time Series Forecasting with Latent Graph Inference