AI 中文总结
针对多元时间序列预测中捕捉全局与局部依赖关系的难题，提出HyBDM多尺度混合模型，通过两个专家分别建模全局模式与局部变化，结合多尺度补丁器和路由器，实验证明其在预测准确性和计算效率上优于现有方法。
AI 中文摘要
时间序列预测（TSF）对许多应用至关重要，但现有模型难以捕捉多元时间序列中的异构远程全局模式和短程局部变化。一些方法部分建模这些依赖关系，但往往不联合利用时间和特征信息。为此提出HyBDM，一种多尺度混合模型，将时间动态分解为全局模式和局部变化，由两个专门专家建模。全局模式专家采用增强的BiConv-Mamba模块，局部变化专家使用局部窗口变压器。还有多尺度补丁器和长短期路由器实现多分辨率表示和专家自适应融合。在六个基准数据集上的实验表明，HyBDM在预测准确性和计算效率上均优于现有方法。
英文摘要
Time series forecasting (TSF) is vital to many applications, yet existing models often struggle to capture the heterogeneous long-range global patterns and short-range local variations in multivariate time series. While some approaches partially model these dependencies, they often do not jointly exploit temporal and feature-wise information. To address this challenge, we propose HyBDM, a multi-scale hybrid model that decomposes temporal dynamics into global patterns and local variations, which are modeled by two specialized experts. The Global Patterns Expert employs an enhanced BiConv-Mamba module that integrates bidirectional convolutions, an M-SSM layer, a forgetting mechanism, and a GDD-MLP module for cross-channel modeling. The Local Variations Expert uses a Local Window Transformer (LWT) to perform efficient locality-aware attention with reduced computational complexity. In addition, a Multi-Scale Patcher and a Long-Short Router enable multi-resolution representations and adaptive fusion of the two experts. Experiments on six benchmark datasets show that HyBDM outperforms state-of-the-art methods in both forecasting accuracy and computational efficiency, demonstrating its effectiveness in bridging global-local dependencies for multivariate TSF.
