论文标题
多元时间序列中时空GNN的稀疏和过滤
Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series
论文作者
论文摘要
我们为多元时间序列预测提出了一个端到端体系结构，该预测将空间 - 周期性图神经网络与矩阵滤波模块集成在一起。该模块在将它们输入到GNN之前从多元时间序列中生成过滤（反）相关图。与图神经网络中采用的现有稀疏方法相反，我们的模型明确利用时间序列过滤来克服复杂系统数据的典型信噪比。我们提供了一组实验，我们可以预测合成时间序列销售数据集的未来销售。所提出的时空图神经网络在基线方法（没有图形信息）以及完全连接的，断开的图和未经过滤的图的情况下显示出卓越的性能。
We propose an end-to-end architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a matrix filtering module. This module generates filtered (inverse) correlation graphs from multivariate time series before inputting them into a GNN. In contrast with existing sparsification methods adopted in graph neural network, our model explicitly leverage time-series filtering to overcome the low signal-to-noise ratio typical of complex systems data. We present a set of experiments, where we predict future sales from a synthetic time-series sales dataset. The proposed spatial-temporal graph neural network displays superior performances with respect to baseline approaches, with no graphical information, and with fully connected, disconnected graphs and unfiltered graphs.
