用扩散模型估算信息流向，突破传统方法的维度与数据瓶颈
TENDE: Transfer Entropy Neural Diffusion Estimation
- 基于得分函数的扩散模型，通过条件互信息估计转移熵
- 在合成与真实数据上均优于现有神经估计算法
- 适合高维时间序列分析，尤其神经科学与金融领域
转移熵衡量时间序列中的定向信息流动，广泛应用于神经科学、金融和复杂系统分析。然而，现有估计方法面临维度诅咒、分布假设严格或需指数级大样本才能可靠收敛等问题。本文提出TENDE（Transfer Entropy Neural Diffusion Estimation），利用基于得分的扩散模型，通过条件互信息估计转移熵。通过学习相关条件分布的得分函数，TENDE实现灵活且可扩展的估计，对底层数据生成过程假设极少。在合成基准和真实数据上，其精度和鲁棒性均显著优于现有神经估计算法及其他先进方法。
原文摘要 · Abstract (English)
Transfer entropy measures directed information flow in time series, and it has become a fundamental quantity in applications spanning neuroscience, finance, and complex systems analysis. However, existing estimation methods suffer from the curse of dimensionality, require restrictive distributional assumptions, or need exponentially large datasets for reliable convergence. We address these limitations in the literature by proposing TENDE (Transfer Entropy Neural Diffusion Estimation), a novel approach that leverages score-based diffusion models to estimate transfer entropy through conditional mutual information. By learning score functions of the relevant conditional distributions, TENDE provides flexible, scalable estimation while making minimal assumptions about the underlying data-generating process. We demonstrate superior accuracy and robustness compared to existing neural estimators and other state-of-the-art approaches across synthetic benchmarks and real data.
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