  Multivariate time series (MTS) forecasting is an essential problem in many
fields. Accurate forecasting results can effectively help decision-making. To
date, many MTS forecasting methods have been proposed and widely applied.
However, these methods assume that the predicted value of a single variable is
affected by all other variables, which ignores the causal relationship among
variables. To address the above issue, we propose a novel end-to-end deep
learning model, termed graph neural network with Neural Granger Causality
(CauGNN) in this paper. To characterize the causal information among variables,
we introduce the Neural Granger Causality graph in our model. Each variable is
regarded as a graph node, and each edge represents the casual relationship
between variables. In addition, convolutional neural network (CNN) filters with
different perception scales are used for time series feature extraction, which
is used to generate the feature of each node. Finally, Graph Neural Network
(GNN) is adopted to tackle the forecasting problem of graph structure generated
by MTS. Three benchmark datasets from the real world are used to evaluate the
proposed CauGNN. The comprehensive experiments show that the proposed method
achieves state-of-the-art results in the MTS forecasting task.
