Dynamic graph structure learning for multivariate time series forecasting

Zhuo Lin Li, Gaowei Zhang, Jie Yu, Lingyu Xu

Abstract: Highlights•We formalize the problem of dependencies in multivariate time series data being a mixture of long- and short-term patterns.•We propose a novel graph learning-neural network to model long- and short-term patterns in data without any priori knowledge.•The propose dynamic graph learning method can capture dynamic spatio-temporal dependencies in short-term patterns.•We show the effectiveness on six public datasets and analyze the learned graph structures.