Graph neural networks (GNNs) have been widely applied in multi-variate time-series forecasting (MTSF) tasks because of their capability in capturing the correlations among different time-series. These graph-based learning approaches improve the forecasting performance by discovering and understanding the underlying graph structures, which represent the data correlation. When the explicit prior graph structures are not available, most existing works cannot guarantee the sparsity of the generated graphs that make the overall model computational expensive and less interpretable. In this work, we propose a decoupled training method, which includes a graph generating module and a GNNs forecasting module. First, we use Graphical Lasso (or GraphLASSO) to directly exploit the sparsity pattern from data to build graph structures in both static and time-varying cases. Second, we fit these graph structures and the input data into a Graph Convolutional Recurrent Network (GCRN) to train a forecasting model. The experimental results on three real-world datasets show that our novel approach has competitive performance against existing state-of-the-art forecasting algorithms while providing sparse, meaningful and explainable graph structures and reducing training time by approximately 40%. Our PyTorch implementation is publicly available at https://github.com/HySonLab/GraphLASSO

翻译：图神经网络（GNNs）因能够捕捉不同时间序列之间的相关性，已被广泛应用于多变量时间序列预测（MTSF）任务。这类基于图的学习方法通过发现并理解代表数据相关性的底层图结构，提升了预测性能。当显式的先验图结构不可用时，现有大多数工作无法保证生成图的稀疏性，导致整体模型计算成本高且可解释性较差。本文提出一种解耦训练方法，包含图生成模块和GNNs预测模块。首先，我们采用Graphical Lasso（或GraphLASSO）直接从数据中挖掘稀疏模式，在静态和时变情况下构建图结构。其次，将这些图结构与输入数据拟合到图卷积循环网络（GCRN）中，训练预测模型。在三个真实世界数据集上的实验结果表明，我们的新方法在提供稀疏、有意义且可解释的图结构的同时，实现了与现有最先进预测算法相当的竞争性能，并将训练时间减少约40%。我们的PyTorch实现已公开于https://github.com/HySonLab/GraphLASSO。