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

• URL: http://arxiv.org/abs/2306.02326v1
• Date: Sun, 4 Jun 2023 10:50:52 GMT
• Title: Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting
• Authors: Donghao Luo, Xue Wang
• Abstract summary: Key to accurate forecasting results is capturing the long-term dependency between each time step. Recent methods mainly focus on the cross-time dependency but seldom consider the cross-variable dependency. We propose a modern pure convolution structure, namely Cross-LKTCN, to better utilize both cross-time and cross-variable dependency.
• Score: 9.433527676880903
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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