用语义结构化分块提升多变量时间序列预测效果
Rethinking Patch Based Multivariate Time Series Forecasting with Semantic Structured Partitioning
- 通过自适应生成语义单元，构建动态语义图组织时间模式
- 在12个真实数据集上显著优于现有方法，最高提升12.3%
- 适合需要捕捉复杂时序依赖的工业预测场景
多变量时间序列预测是众多实际应用中的基础任务。现有基于分块的方法主要分为三类：固定分块会破坏有意义的时间边界，多尺度分块可能引入跨尺度冗余表示，可扩展分块虽增强灵活性但仍缺乏显式语义结构组织机制与异构时序模式间交互建模能力。为此，我们提出SCPaT——一种基于语义结构化分块的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，再构建动态语义图以建模这些单元间的有向依赖关系，并将其组织为高阶语义块。基于这些结构化表示，重要性感知路由机制将不同语义块自适应分配给不同专家进行定制化建模。在12个真实世界数据集上的大量实验验证了SCPaT的有效性。
原文摘要 · Abstract (English)
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.
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