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.

多分辨率卷积和可变形卷积操作的自适应时变卷积网络（ATVCNet）在多变量时间序列的局部 / 全局时间依赖和变量间依赖建模方面取得了显著改进，通过不同扩张因子的卷积核扩大感受野以捕捉不同分辨率间的时间相关信息，并通过附加的偏移向量自适应调整采样位置，增强网络捕捉变量间相关特征的能力，通过提取和融合不同分辨率的时间序列特征，在时间序列中捕捉到了局部背景信息和全局模式，并且设计的变量间特征自适应提取模块捕捉了时间序列中不同变量间的相关性，实验结果表明，ATVCNet 相较于现有的 MLSTF 模型性能提高了近 63.4%。