Multivariate Time Series Forecasting with Latent Graph Inference

Understand

This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series.

• Its modularity allows it to be integrated with current univariate methods.
• Our approach allows to trade-off accuracy and computational efficiency gradually via offering on one extreme inference of a potentially fully-connected graph or on another extreme a bipartite graph.
• In the potentially fully-connected case we consider all pair-wise interactions among time-series which yields the best forecasting accuracy.