MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate
  Time Series Forecasting
Wanlin Cai Yuxuan Liang Xianggen Liu Jianshuai Feng Yuankai Wu
Multivariate time series forecasting poses an ongoing challenge across
various disciplines. Time series data often exhibit diverse intra-series and
inter-series correlations, contributing to intricate and interwoven
dependencies that have been the focus of numerous studies. Nevertheless, a
significant research gap remains in comprehending the varying inter-series
correlations across different time scales among multiple time series, an area
that has received limited attention in the literature. To bridge this gap, this
paper introduces MSGNet, an advanced deep learning model designed to capture
the varying inter-series correlations across multiple time scales using
frequency domain analysis and adaptive graph convolution. By leveraging
frequency domain analysis, MSGNet effectively extracts salient periodic
patterns and decomposes the time series into distinct time scales. The model
incorporates a self-attention mechanism to capture intra-series dependencies,
while introducing an adaptive mixhop graph convolution layer to autonomously
learn diverse inter-series correlations within each time scale. Extensive
experiments are conducted on several real-world datasets to showcase the
effectiveness of MSGNet. Furthermore, MSGNet possesses the ability to
automatically learn explainable multi-scale inter-series correlations,
exhibiting strong generalization capabilities even when applied to
out-of-distribution samples.