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Graph neural networks (GNNs) have been widely applied in multi-variatetime-series forecasting (MTSF) tasks because of their capability in capturingthe correlations among different time-series. These graph-based learningapproaches improve the forecasting performance by discovering and understandingthe underlying graph structures, which represent the data correlation. When theexplicit prior graph structures are not available, most existing works cannotguarantee the sparsity of the generated graphs that make the overall modelcomputational expensive and less interpretable. In this work, we propose adecoupled training method, which includes a graph generating module and a GNNsforecasting module. First, we use Graphical Lasso (or GraphLASSO) to directlyexploit the sparsity pattern from data to build graph structures in both staticand time-varying cases. Second, we fit these graph structures and the inputdata into a Graph Convolutional Recurrent Network (GCRN) to train a forecastingmodel. The experimental results on three real-world datasets show that ournovel approach has competitive performance against existing state-of-the-artforecasting algorithms while providing sparse, meaningful and explainable graphstructures and reducing training time by approximately 40%. Our PyTorchimplementation is publicly available at https://github.com/HySonLab/GraphLASSO

Sparsity exploitation via discovering graphical models in multi-variate time-series forecasting