  The multivariate time series forecasting has attracted more and more
attention because of its vital role in different fields in the real world, such
as finance, traffic, and weather. In recent years, many research efforts have
been proposed for forecasting multivariate time series. Although some previous
work considers the interdependencies among different variables in the same
timestamp, existing work overlooks the inter-connections between different
variables at different time stamps. In this paper, we propose a simple yet
efficient instance-wise graph-based framework to utilize the inter-dependencies
of different variables at different time stamps for multivariate time series
forecasting. The key idea of our framework is aggregating information from the
historical time series of different variables to the current time series that
we need to forecast. We conduct experiments on the Traffic, Electricity, and
Exchange-Rate multivariate time series datasets. The results show that our
proposed model outperforms the state-of-the-art baseline methods.
