Multivariate time-series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-series temporal correlations and inter-series correlations simultaneously. Recently, there have been multiple works trying to capture both correlations, but most, if not all of them only capture temporal correlations in the time domain and resort to pre-defined priors as inter-series relationships. In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting. StemGNN captures inter-series correlations and temporal dependencies \textit{jointly} in the \textit{spectral domain}. It combines Graph Fourier Transform (GFT) which models inter-series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework. After passing through GFT and DFT, the spectral representations hold clear patterns and can be predicted effectively by convolution and sequential learning modules. Moreover, StemGNN learns inter-series correlations automatically from the data without using pre-defined priors. We conduct extensive experiments on ten real-world datasets to demonstrate the effectiveness of StemGNN. Code is available at https://github.com/microsoft/StemGNN/

翻译：多变时间序列预测在许多现实世界应用中发挥着关键作用。 这是一个具有挑战性的问题,因为人们需要同时考虑序列内部的时间相关性和序列间的相关性。 最近,人们做了多项工作试图捕捉这两种相关性,但大多数,如果不是全部的话,都只捕捉时间域的时间相关性,并采用预定义的先行时间序列关系作为跨系列关系。在本文件中,我们提议光谱时空图神经网络(StemGNNN),以进一步提高多变时间序列预测的准确性。 StemGNNNN需要同时在\ textit{ 光谱域} 中同时捕捉跨系列相关性和时间依赖性\ textit{共同} 。它把模拟时间域域际相关性和分变异变(DFT)的图四变(GTT)结合起来,在端对端和DFTFT框架进行模拟时间依赖性关系。在通过GFT和DFT后,光谱显示清晰的模式,并且可以通过同级和顺序学习模块有效地预测。此外, StemGNNNNN在不使用以前的数据自动地展示我们定义的滚动式/G系统上的数据。