Instance-wise Graph-based Framework for Multivariate Time Series Forecasting
This work addresses forecasting problems in domains like finance and traffic, but it appears incremental as it builds on existing graph-based methods by extending them to cross-timestamp dependencies.
The paper tackles multivariate time series forecasting by addressing overlooked inter-connections between variables across different timestamps, proposing an instance-wise graph-based framework that aggregates historical information, and reports results showing it outperforms state-of-the-art baselines on Traffic, Electricity, and Exchange-Rate datasets.
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.