The paper "Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks" by Zonghan Wu addresses the challenge of modeling multivariate time series data, where variables are interdependent. Traditional methods often fail to fully exploit these dependencies, and while graph neural networks (GNNs) are effective in handling relational dependencies, they require well-defined graph structures, which are not always available for multivariate time series data. The authors propose a novel framework, MTGNN, designed specifically for multivariate time series forecasting. This framework includes a graph learning layer, graph convolution modules, and temporal convolution modules. The graph learning layer automatically extracts uni-directed relationships among variables, while the graph convolution modules capture spatial dependencies, and the temporal convolution modules capture temporal patterns. The framework is trained end-to-end, jointly learning the graph structure and the GNN parameters. Experimental results show that MTGNN outperforms state-of-the-art methods on four benchmark datasets and achieves competitive performance on two traffic datasets, demonstrating its effectiveness in handling multivariate time series data with or without predefined graph structures.The paper "Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks" by Zonghan Wu addresses the challenge of modeling multivariate time series data, where variables are interdependent. Traditional methods often fail to fully exploit these dependencies, and while graph neural networks (GNNs) are effective in handling relational dependencies, they require well-defined graph structures, which are not always available for multivariate time series data. The authors propose a novel framework, MTGNN, designed specifically for multivariate time series forecasting. This framework includes a graph learning layer, graph convolution modules, and temporal convolution modules. The graph learning layer automatically extracts uni-directed relationships among variables, while the graph convolution modules capture spatial dependencies, and the temporal convolution modules capture temporal patterns. The framework is trained end-to-end, jointly learning the graph structure and the GNN parameters. Experimental results show that MTGNN outperforms state-of-the-art methods on four benchmark datasets and achieves competitive performance on two traffic datasets, demonstrating its effectiveness in handling multivariate time series data with or without predefined graph structures.