Instance-wise Graph-based Framework for Multivariate Time Series Forecasting

TL;DR

This paper introduces an instance-wise graph-based framework that leverages inter-variable dependencies across different timestamps to improve multivariate time series forecasting accuracy, outperforming existing methods.

Contribution

The proposed framework uniquely models inter-variable dependencies across timestamps, enhancing forecasting performance in multivariate time series analysis.

Findings

Outperforms state-of-the-art baseline methods

Effective in traffic, electricity, and exchange-rate datasets

Utilizes inter-dependencies across timestamps

Abstract

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.