Multivariate Time Series Forecasting with Latent Graph Inference
This addresses forecasting efficiency for multivariate time series data, offering incremental improvements in computational trade-offs.
The paper tackles multivariate time series forecasting by jointly inferring and leveraging relations among time series, introducing a method that trades off accuracy and computational efficiency, with variants performing better or competitively than previous graph inference methods in accuracy and time efficiency.
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. Conversely, the bipartite case leverages the dependency structure by inter-communicating the N time series through a small set of K auxiliary nodes that we introduce. This reduces the time and memory complexity w.r.t. previous graph inference methods from O(N^2) to O(NK) with a small trade-off in accuracy. We demonstrate the effectiveness of our model in a variety of datasets where both of its variants perform better or very competitively to previous graph inference methods in terms of forecasting accuracy and time efficiency.