Time series data in real-world scenarios contain a substantial amount of nonlinear information, which significantly interferes with the training process of models, leading to decreased prediction performance. Therefore, during the time series forecasting process, extracting the local and global time series patterns and understanding the potential nonlinear features among different time observations 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 at different resolutions, and adaptively adjusting the sampling positions through additional offset vectors, we enhance the network's ability to capture potential nonlinear features among time observations. Building upon this, we propose ACNet, an adaptive convolutional network designed to effectively model the local and global temporal dependencies and the nonlinear features between observations in multivariate time series. Specifically, by extracting and fusing time series features at different resolutions, we capture both local contextual information and global patterns in the time series. The designed nonlinear feature adaptive extraction module captures the nonlinear features among different time observations in the time series. We evaluated the performance of ACNet across twelve real-world datasets. The results indicate that ACNet consistently achieves state-of-the-art performance in both short-term and long-term forecasting tasks with favorable runtime efficiency.

翻译：现实场景中的时序数据包含大量非线性信息，这些信息会显著干扰模型的训练过程，导致预测性能下降。因此，在时序预测过程中，提取时序的局部与全局模式，并理解不同时间观测点之间潜在的非线性特征具有十分重要的意义。为应对这一挑战，我们引入了多分辨率卷积与可变形卷积操作：通过采用不同膨胀系数的卷积核扩大感受野以捕获不同分辨率下的时序相关性信息，并借助额外的偏移向量自适应调整采样位置，从而增强网络对时间观测点间潜在非线性特征的捕捉能力。在此基础上，我们提出了ACNet——一种自适应卷积网络，旨在有效建模多元时间序列中的局部与全局时序依赖关系以及观测点间的非线性特征。具体而言，通过提取并融合不同分辨率的时序特征，我们同时捕获了时序中的局部上下文信息与全局模式。所设计的非线性特征自适应提取模块能够捕捉时序中不同时间观测点之间的非线性特征。我们在十二个真实世界数据集上评估了ACNet的性能。结果表明，ACNet在短期与长期预测任务中均能持续取得最先进的性能，且具有较好的运行效率。