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

TL;DR

Cross-LKTCN introduces a novel convolutional architecture that effectively captures both cross-time and cross-variable dependencies, significantly improving multivariate time series forecasting accuracy over existing methods.

Contribution

The paper presents a new pure convolutional model, Cross-LKTCN, that simultaneously models cross-time and cross-variable dependencies, addressing a gap in current forecasting techniques.

Findings

Achieves state-of-the-art forecasting performance on real-world benchmarks.

Significantly outperforms existing convolutional and cross-variable models.

Effectively captures long-term dependencies in multivariate time series.

Abstract

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.