The past few years have witnessed the rapid development in multivariate time
series forecasting. The key to accurate forecasting results is capturing the
long-term dependency between each time step (cross-time dependency) and
modeling the complex dependency between each variable (cross-variable
dependency) in multivariate time series. However, recent methods mainly focus
on the cross-time dependency but seldom consider the cross-variable dependency.
To fill this gap, we find that convolution, a traditional technique but
recently losing steam in time series forecasting, meets the needs of
respectively capturing the cross-time and cross-variable dependency. Based on
this finding, we propose a modern pure convolution structure, namely
Cross-LKTCN, to better utilize both cross-time and cross-variable dependency
for time series forecasting. Specifically in each Cross-LKTCN block, a
depth-wise large kernel convolution with large receptive field is proposed to
capture cross-time dependency, and then two successive point-wise group
convolution feed forward networks are proposed to capture cross-variable
dependency. Experimental results on real-world benchmarks show that Cross-LKTCN
achieves state-of-the-art forecasting performance and improves the forecasting
accuracy significantly compared with existing convolutional-based models and
cross-variable methods.

本文提出了一种基于卷积神经网络和大内核的先进纯卷积结构，用于捕捉多变量时间序列中的跨时间和跨变量依赖关系，实验结果表明这种方法在时间序列预测中获得了最先进的性能。

跨变量依赖模型的现代卷积在多元时间序列预测中的应用

Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for  Multivariate Time Series Forecasting Dependency for Multivariate Time Series  Forecasting