AI 中文总结
针对现有补丁式多元时间序列预测方法的局限，提出基于语义结构化划分的Transformer框架SCPaT，通过自适应语义单元生成等机制建模序列，在12个真实数据集上验证了有效性。
AI 中文摘要
多元时间序列预测（MTSF）是众多现实应用中的基础任务。现有基于补丁的预测方法通常分为三类：固定划分、多尺度划分和可扩展划分。固定划分常破坏有意义的时间边界，多尺度划分可能引入跨尺度的冗余表示，可扩展划分提升了灵活性，但仍缺乏组织语义结构和建模异质时间模式间交互的显式机制。为解决这些局限，我们提出SCPaT，一种基于语义结构化划分的Transformer框架。SCPaT首先通过自适应语义单元生成将输入序列分解为语义一致的单元，接着构建动态语义图以建模这些单元间的有向依赖并将其组织为高阶语义块。基于这些结构化表示，一种感知重要性的路由机制自适应地将不同语义块分配给不同专家以进行定制化建模。在12个现实世界数据集上的大量实验证明了SCPaT的有效性。
英文摘要
Multivariate time series forecasting (MTSF) is a fundamental task in many real world applications. Existing patch based forecasting methods generally fall into three categories: fixed partitioning, multi-scale partitioning, and extendable partitioning. Fixed partitioning often breaks meaningful temporal boundaries, multi-scale partitioning may introduce redundant representations across scales, and extendable partitioning improves flexibility but still lacks an explicit mechanism for organizing semantic structure and modeling interactions among heterogeneous temporal patterns. To address these limitations, we propose SCPaT, a Transformer based framework built on semantic structured partitioning. SCPaT first decomposes input sequences into semantically consistent units through adaptive semantic unit generation, then constructs a dynamic semantic graph to model directed dependencies among these units and organize them into higher order semantic blocks. Based on these structured representations, an importance aware routing mechanism adaptively dispatches different semantic blocks to different experts for customized modeling. Extensive experiments on 12 real world datasets demonstrate the effectiveness of SCPaT.
