In multivariate time series forecasting, the Transformer architecture encounters two significant challenges: effectively mining features from historical sequences and avoiding overfitting during the learning of temporal dependencies. To tackle these challenges, this paper deconstructs time series forecasting into the learning of historical sequences and prediction sequences, introducing the Cross-Variable and Time Network (CVTN). This unique method divides multivariate time series forecasting into two phases: cross-variable learning for effectively mining fea tures from historical sequences, and cross-time learning to capture the temporal dependencies of prediction sequences. Separating these two phases helps avoid the impact of overfitting in cross-time learning on cross-variable learning. Exten sive experiments on various real-world datasets have confirmed its state-of-the-art (SOTA) performance. CVTN emphasizes three key dimensions in time series fore casting: the short-term and long-term nature of time series (locality and longevity), feature mining from both historical and prediction sequences, and the integration of cross-variable and cross-time learning. This approach not only advances the current state of time series forecasting but also provides a more comprehensive framework for future research in this field.

翻译：在多变量时间序列预测中，Transformer架构面临两大挑战：有效挖掘历史序列特征以及在学习时间依赖关系时避免过拟合。针对这些问题，本文将时间序列预测解构为历史序列学习与预测序列学习两个部分，提出跨变量与时间网络（CVTN）。该独特方法将多变量时间序列预测分为两个阶段：跨变量学习阶段用于有效挖掘历史序列特征，以及跨时间学习阶段用于捕获预测序列的时间依赖关系。分离这两个阶段有助于避免跨时间学习中的过拟合对跨变量学习造成影响。在多种真实世界数据集上的广泛实验证实了其当前最优（SOTA）性能。CVTN强调时间序列预测的三个关键维度：时间序列的短期性与长期性（局部性与长期性）、对历史序列与预测序列的特征挖掘，以及跨变量与跨时间学习的整合。该方法不仅推动了时间序列预测领域的最新进展，也为该领域的未来研究提供了更全面的框架。