标题： 动态稀疏因果注意力时序网络用于多变量时间序列中的可解释因果发现 显示英文标题

标题： Dynamic Sparse Causal-Attention Temporal Networks for Interpretable Causality Discovery in Multivariate Time Series

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有效的决策至关重要，其中复杂的依赖关系和滞后效应挑战了传统的分析方法。 我们引入了动态稀疏因果注意力时序网络，用于多变量时间序列中可解释的因果发现（DyCAST-Net），这是一种新架构，旨在通过整合扩张时间卷积和动态稀疏注意力机制来增强因果发现。 DyCAST-Net通过扩张卷积有效捕捉多尺度时间依赖性，同时在其注意力机制中利用自适应阈值策略以消除虚假连接，确保准确性和可解释性。 统计洗牌测试验证进一步增强了鲁棒性，通过过滤假阳性结果并提高因果推断的可靠性。 在金融和营销数据集上的广泛评估表明，DyCAST-Net始终优于现有模型，如TCDF、GCFormer和CausalFormer。 该模型提供了更精确的因果延迟估计，并显著减少了假发现，特别是在噪声环境中。 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。 案例研究展示了DyCAST-Net检测潜在中介因素和滞后因果因素的能力，使其在高维、动态环境中特别有效。 通过RMSNorm稳定性和因果掩码增强的模型架构确保了在不同应用领域的可扩展性和适应性。 显示英文摘要

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