D-CTNet: 一种双分支通道-时间预测网络与频域校正
D-CTNet: A Dual-Branch Channel-Temporal Forecasting Network with Frequency-Domain Correction
AI总结：
D-CTNet通过双分支结构和频域校正机制，提升多变量时间序列预测的准确性和鲁棒性。
摘要：
准确的多变量时间序列（MTS）预测对于复杂系统的协同设计、数字孪生构建以及提前维护至关重要。然而，协作工业环境为MTS预测模型带来了新的挑战：模型需要解耦复杂的变量依赖关系，同时应对环境变化带来的非平稳分布偏移。为解决这些挑战并提高协同感知的可靠性，我们提出了一种基于补丁的双分支通道-时间预测网络（D-CTNet）。特别是，通过并行双分支设计，结合线性时间建模层和通道注意力机制，我们的方法显式解耦并联合学习通道内的时间演变模式和动态多变量相关性。此外，一个全局补丁注意力融合模块超越了局部窗口范围，以建模长距离依赖性。最重要的是，针对非平稳性，一种频域平稳性校正机制通过频谱对齐，自适应地抑制环境变化带来的分布偏移影响。在七个基准数据集上的评估表明，我们的模型在预测精度和鲁棒性方面优于最先进的方法。我们的工作展示出作为工业协同系统新预测引擎的巨大潜力。
英文摘要：
Accurate Multivariate Time Series (MTS) forecasting is crucial for collaborative design of complex systems, Digital Twin building, and maintenance ahead of time. However, the collaborative industrial environment presents new challenges for MTS forecasting models: models should decouple complex inter-variable dependencies while addressing non-stationary distribution shift brought by environmental changes. To address these challenges and improve collaborative sensing reliability, we propose a Patch-Based Dual-Branch Channel-Temporal Forecasting Network (D-CTNet). Particularly, with a parallel dual-branch design incorporating linear temporal modeling layer and channel attention mechanism, our method explicitly decouples and jointly learns intra-channel temporal evolution patterns and dynamic multivariate correlations. Furthermore, a global patch attention fusion module goes beyond the local window scope to model long range dependencies. Most importantly, aiming at non-stationarity, a Frequency-Domain Stationarity Correction mechanism adaptively suppresses distribution shift impacts from environment change by spectrum alignment. Evaluations on seven benchmark datasets show that our model achieves better forecasting accuracy and robustness compared with state-of-the-art methods. Our work shows great promise as a new forecasting engine for industrial collaborative systems.
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。
