Models employing CNN architecture have made significant progress in multivariate long sequence time-series forecasting (MLSTF), particularly in modeling local time series characteristics. However, during the MLSTF process, extracting the global time series patterns and understanding the correlations among different variables are highly significant. To address this challenge, we introduce multi-resolution convolution and deformable convolution operations. By enlarging the receptive field using convolution kernels with different dilation factors to capture temporal correlation information across different resolutions, and adaptively adjusting the sampling positions through additional offset vectors, we enhance the network's ability to capture correlated features between variables. Building upon this, we propose ATVCNet, an adaptive temporal-variable convolutional network designed to effectively model the local/global temporal dependencies and inter-variable dependencies of multivariate time series. Specifically, extracting and fusing time series features at different resolutions, captures both local contextual information and global patterns in the time series. The designed inter-variable feature adaptive extraction module captures the correlation among different variables in the time series. We evaluated the performance of ATVCNet across eight real-world datasets. The results indicate that ATVCNet achieved a performance improvement of approximately 63.4% over state-of-the-art MLSTF models.

翻译：采用CNN架构的模型在多元长序列时间序列预测（MLSTF）中取得了显著进展，尤其在局部时间序列特征建模方面表现出色。然而，在MLSTF过程中，提取全局时间序列模式并理解不同变量之间的相关性具有重要意义。为解决这一挑战，我们引入了多分辨率卷积和可变形卷积操作。通过使用不同扩张因子的卷积核扩大感受野以捕获跨分辨率的时间相关性信息，并借助额外的偏移向量自适应调整采样位置，增强了网络捕获变量间相关特征的能力。在此基础上，我们提出了ATVCNet，一种自适应时间-变量卷积网络，旨在有效建模多元时间序列的局部/全局时间依赖性和变量间依赖性。具体而言，在不同分辨率下提取和融合时间序列特征，既能捕获时间序列中的局部上下文信息，也能把握全局模式。设计的变量间特征自适应提取模块能够捕获时间序列中不同变量之间的相关性。我们在八个真实世界数据集上评估了ATVCNet的性能。结果表明，ATVCNet相较于最先进的MLSTF模型实现了约63.4%的性能提升。