用于多变量时间序列可解释因果发现的动态稀疏因果注意力时间网络
Dynamic Sparse Causal-Attention Temporal Networks for Interpretable Causality Discovery in Multivariate Time Series
AI总结：
针对多变量时间序列因果发现难题，提出DyCAST-Net架构，结合膨胀卷积与动态稀疏注意力过滤虚假连接，在金融与营销数据集上优于现有模型，提供更精确的因果延迟估计与可解释性。
摘要：
理解多变量时间序列（MTS）中的因果关系对于金融和营销等领域的有效决策至关重要，在这些领域中，复杂的依赖关系和滞后效应对传统分析方法构成了挑战。我们提出了用于MTS可解释因果发现的动态稀疏因果注意力时间网络（DyCAST-Net），这是一种通过集成膨胀时间卷积和动态稀疏注意力机制来增强因果发现的新颖架构。DyCAST-Net通过膨胀卷积有效捕获多尺度时间依赖关系，同时在其注意力机制中利用自适应阈值策略消除虚假连接，从而确保了准确性和可解释性。统计置乱检验验证通过过滤假阳性并提高因果推断的可靠性，进一步增强了鲁棒性。在金融和营销数据集上的广泛评估表明，DyCAST-Net始终优于TCDF、GCFormer和CausalFormer等现有模型。该模型提供了更精确的因果延迟估计，并显著减少了错误发现，特别是在嘈杂环境中。此外，注意力热图提供了可解释的见解，揭示了隐藏的因果模式，例如广告对消费者行为的中介效应以及宏观经济指标对金融市场的影响。案例研究说明了DyCAST-Net检测潜在中介变量和滞后因果因子的能力，使其在高维、动态环境中尤为有效。该模型架构通过RMSNorm稳定和因果掩码增强，确保了在多样化应用领域的可扩展性和适应性。
英文摘要：
Understanding causal relationships in multivariate time series (MTS) is essential for effective decision-making in fields such as finance and marketing, where complex dependencies and lagged effects challenge conventional analytical approaches. We introduce Dynamic Sparse Causal-Attention Temporal Networks for Interpretable Causality Discovery in MTS (DyCAST-Net), a novel architecture designed to enhance causal discovery by integrating dilated temporal convolutions and dynamic sparse attention mechanisms. DyCAST-Net effectively captures multiscale temporal dependencies through dilated convolutions while leveraging an adaptive thresholding strategy in its attention mechanism to eliminate spurious connections, ensuring both accuracy and interpretability. A statistical shuffle test validation further strengthens robustness by filtering false positives and improving causal inference reliability. Extensive evaluations on financial and marketing datasets demonstrate that DyCAST-Net consistently outperforms existing models such as TCDF, GCFormer, and CausalFormer. The model provides a more precise estimation of causal delays and significantly reduces false discoveries, particularly in noisy environments. Moreover, attention heatmaps offer interpretable insights, uncovering hidden causal patterns such as the mediated effects of advertising on consumer behavior and the influence of macroeconomic indicators on financial markets. Case studies illustrate DyCAST-Net's ability to detect latent mediators and lagged causal factors, making it particularly effective in high-dimensional, dynamic settings. The model's architecture enhanced by RMSNorm stabilization and causal masking ensures scalability and adaptability across diverse application domains
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