Cross-LKTCN：利用跨变量依赖进行多变量时间序列预测的现代卷积
Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting
AI总结：
针对多变量时间序列预测中跨变量依赖建模不足的问题，提出纯卷积结构 Cross-LKTCN，通过大核卷积与分组逐点卷积联合建模跨时间和跨变量依赖，在基准上达到最先进性能。
摘要：
过去几年，多变量时间序列预测取得了快速发展。获得准确预测结果的关键在于捕捉各时间步之间的长期依赖（跨时间依赖），并对多变量时间序列中各变量之间的复杂依赖（跨变量依赖）进行建模。然而，近期方法主要关注跨时间依赖，却很少考虑跨变量依赖。为填补这一空白，我们发现卷积这一传统技术（尽管近期在时间序列预测中逐渐失势）能够满足分别捕捉跨时间依赖和跨变量依赖的需求。基于这一发现，我们提出了一种现代纯卷积结构，即 Cross-LKTCN，以更好地利用跨时间和跨变量依赖进行时间序列预测。具体而言，在每个 Cross-LKTCN 块中，提出具有大感受野的深度大核卷积来捕捉跨时间依赖，随后提出两个连续的逐点分组卷积前馈网络来捕捉跨变量依赖。在真实世界基准上的实验结果表明，与现有基于卷积的模型和跨变量方法相比，Cross-LKTCN 取得了最先进的预测性能，并显著提高了预测准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
