论文标题
通过潜在图推理预测多元时间序列
Multivariate Time Series Forecasting with Latent Graph Inference
论文作者
论文摘要
本文介绍了一种新的多元时间序列预测方法，该方法共同渗透和利用时间序列之间的关系。它的模块化使其可以与当前的单变量方法集成。我们的方法可以通过在潜在的完全连接的图或另一个极端的两部分图上提出来逐渐折衷的准确性和计算效率。在潜在的完全连接的情况下，我们考虑了时间序列之间的所有配对相互作用，这具有最佳的预测准确性。相反，双方案例通过通过我们引入的一小部分k辅助节点进行n时间序列来利用依赖关系结构。这降低了时间和内存复杂性W.R.T.先前的图推理方法从O（n^2）到O（NK），精度的折衷很小。我们在各种数据集中证明了我们的模型的有效性，在这些数据集中，其两个变体在预测准确性和时间效率方面对以前的图推理方法的表现更好或竞争性。
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.
