CVTN：用于时间序列预测的跨变量与时间集成
CVTN: Cross Variable and Temporal Integration for Time Series Forecasting
AI总结：
本文提出跨变量与时间网络（CVTN），将多变量时间序列预测解构为跨变量学习与跨时间学习两阶段，有效避免了过拟合，在多个真实数据集上实现了SOTA性能。
摘要：
在多变量时间序列预测中，Transformer架构面临两个重大挑战：有效地从历史序列中挖掘特征，以及在时间依赖性学习过程中避免过拟合。为了解决这些挑战，本文将时间序列预测解构为历史序列学习和预测序列学习，并提出了跨变量与时间网络（CVTN）。这一独特方法将多变量时间序列预测分为两个阶段：跨变量学习以有效地从历史序列中挖掘特征，以及跨时间学习以捕捉预测序列的时间依赖性。将这两个阶段分离有助于避免跨时间学习中的过拟合对跨变量学习的影响。在多个真实世界数据集上的广泛实验证实了其最先进（SOTA）的性能。CVTN强调时间序列预测中的三个关键维度：时间序列的短期和长期性质（局部性和长期性）、从历史和预测序列中挖掘特征，以及跨变量与跨时间学习的集成。这种方法不仅推进了时间序列预测的当前技术水平，还为该领域的未来研究提供了一个更全面的框架。
英文摘要：
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.
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。
