SEED：基于谱熵的时空依赖性评估用于多变量时间序列预测
SEED: Spectral Entropy-Guided Evaluation of SpatialTemporal Dependencies for Multivariate Time Series Forecasting
AI总结：
SEED通过引入谱熵引导的依赖评估框架，有效解决多变量时间序列预测中变量间依赖建模的问题，提升模型对时空依赖性的适应能力与预测性能。
摘要：
有效的多变量时间序列预测通常受益于准确建模复杂的变量间依赖关系。然而，现有基于注意力或图的方法面临三个关键问题：（a）强时间自依赖性常被无关变量破坏；（b）softmax归一化忽略并反转负相关；（c）变量难以感知其时间位置。为解决这些问题，我们提出了SEED，一种用于时空依赖性建模的谱熵引导评估框架。SEED引入了一个依赖评估器，这是关键创新，利用谱熵动态提供每个变量的空间和时间依赖性的初步评估，使模型能够自适应地平衡通道独立性（CI）和通道依赖性（CD）策略。为了考虑其他变量影响而非内在动态产生的时间规律性，我们提出基于谱熵的融合器，进一步细化评估的依赖权重，有效分离这部分。此外，为了保留负相关，我们引入了一个带符号图构造器，使边权具有符号，克服softmax的限制。最后，为了帮助变量感知其时间位置并从而构建更全面的空间特征，我们引入了上下文空间提取器，利用局部上下文窗口提取空间特征。在12个现实世界数据集上的大量实验表明，SEED实现了最先进的性能，验证了其有效性和通用性。
英文摘要：
Effective multivariate time series forecasting often benefits from accurately modeling complex inter-variable dependencies. However, existing attention- or graph-based methods face three key issues: (a) strong temporal self-dependencies are often disrupted by irrelevant variables; (b) softmax normalization ignores and reverses negative correlations; (c) variables struggle to perceive their temporal positions. To address these, we propose \textbf{SEED}, a Spectral Entropy-guided Evaluation framework for spatial-temporal Dependency modeling. SEED introduces a Dependency Evaluator, a key innovation that leverages spectral entropy to dynamically provide a preliminary evaluation of the spatial and temporal dependencies of each variable, enabling the model to adaptively balance Channel Independence (CI) and Channel Dependence (CD) strategies. To account for temporal regularities originating from the influence of other variables rather than intrinsic dynamics, we propose Spectral Entropy-based Fuser to further refine the evaluated dependency weights, effectively separating this part. Moreover, to preserve negative correlations, we introduce a Signed Graph Constructor that enables signed edge weights, overcoming the limitations of softmax. Finally, to help variables perceive their temporal positions and thereby construct more comprehensive spatial features, we introduce the Context Spatial Extractor, which leverages local contextual windows to extract spatial features. Extensive experiments on 12 real-world datasets from various application domains demonstrate that SEED achieves state-of-the-art performance, validating its effectiveness and generality.
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。
