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.

使用 Cross-Variable and Time Network (CVTN) 方法来解决多变量时间序列预测中的特征挖掘和避免过拟合的挑战，并在各种真实数据集上证实了其卓越性能。该方法强调时间序列预测的短期和长期性质、从历史和预测序列中挖掘特征以及跨变量和跨时间学习的整合。