发表机构
Institute of Collaborative Innovation; University of Macau（协作创新研究所; 澳门大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多元时间序列分析难题，提出MSC-OT方法，结合多尺度卷积与最优传输注意力，由多尺度卷积增强、最优传输正则化及自适应融合策略构成，在多数据集实验中表现良好，验证了组件有效性与协同作用。
AI 中文摘要
多元时间序列（MTS）分析在许多实际应用中起着重要作用，但在捕捉多粒度结构模式和适当抑制噪声方面仍存在挑战。本文提出了具有最优传输注意力的多尺度卷积（MSC-OT）。它结合了多尺度卷积与基于反向嵌入的Sinkhorn最优传输方法，由多尺度卷积增强和Sinkhorn最优传输正则化两部分组成，还采用自适应融合策略。在多个数据集上的实验表明，MSC-OT在短期和长期预测任务中均表现良好，消融实验进一步验证了各组件的有效性及其协同作用。
英文摘要
The analysis of Multivariate Time Series (MTS) plays an important role in a lot of real-world practical applications, but it still remains some challenging problem about capturing multi-granularity structural patterns and suppressing noise appropriately. Multi-Scale Convolution with Optimal Transport Attention (MSC-OT) is proposed in this paper. MSC-OT is a useful architecture to optimize the attention mechanism. It combines multi-scale convolution with Sinkhorn optimal transport method based on inverted embedding. The inverted embedding approach embeds each variable as a token and allows the model to capture cross-variate relationships better. MSC-OT consists of two part: (1) Multi-Scale Convolution Enhancement, that applies multi-scale convolutions to attention score matrices based on inverted embedding, capturing local structural patterns in the variate-interaction space induced by compressed temporal representations; (2) Sinkhorn Optimal Transport Regularization, that formulates attention computation as an optimal transport problem and employs iterative matrix scaling to ensure balanced information flow across variates. Adaptive Fusion Strategy utilizes softmax-normalized learnable weights to dynamically combine base attention, convolution-enhanced, and OT-regularized scores. Experiments on widely-used datasets, including ETT, Electricity, Traffic, Solar-Energy, and Exchange-Rate, show that MSC-OT achieves well performance in both short-term and long-term forecasting tasks. Ablation experiments further validate the effectiveness of each proposed component and their synergistic contributions to improving prediction accuracy for multivariate time series forecasting.
