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.

一种支持多元时间序列预测的灵活模型，可以基于完全联通或双部分图推断时间序列之间的关系，提供高精度或高效率预测并降低时间和内存复杂度。在多个数据集中表现优于之前的图推断方法。