利用幂律谱特征提升时间序列因果发现的抗噪能力
Robust Causal Discovery in Real-World Time Series with Power-Laws
- 从真实数据的幂律频谱特性出发，提取强化因果信号的特征
- 在合成与真实数据上均显著优于现有方法
- 适合金融、气候等含噪声复杂系统中的因果分析
探索随机时间序列中的因果关系是一项具有广泛应用价值但极具挑战性的任务，涉及金融、经济、神经科学和气候科学等领域。尽管已有诸多因果发现（CD）算法提出，但它们对噪声敏感，常导致真实数据中产生虚假因果推断。本文观察到许多真实世界时间序列的频谱分布符合幂律特性，这源于其内在的自组织行为。基于此，我们提出一种基于幂律谱特征提取的鲁棒因果发现方法，能有效放大真实的因果信号。该方法在合成基准与具有已知因果结构的真实数据集上均持续优于当前最优方法，验证了其鲁棒性与实际应用价值。
原文摘要 · Abstract (English)
Exploring causal relationships in stochastic time series is a challenging yet crucial task with a vast range of applications, including finance, economics, neuroscience, and climate science. Many algorithms for Causal Discovery (CD) have been proposed; however, they often exhibit a high sensitivity to noise, resulting in spurious causal inferences in real data. In this paper, we observe that the frequency spectra of many real-world time series follow a power-law distribution, notably due to an inherent self-organizing behavior. Leveraging this insight, we build a robust CD method based on the extraction of power-law spectral features that amplify genuine causal signals. Our method consistently outperforms state-of-the-art alternatives on both synthetic benchmarks and real-world datasets with known causal structures, demonstrating its robustness and practical relevance.
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