论文标题
财务数据的因果推理和机制集群的时间序列K-均值
Time-Series K-means in Causal Inference and Mechanism Clustering for Financial Data
论文作者
论文摘要
本文研究了时间序列K-均值（TS-K-均值）在因果推理和财务时间序列数据集群的背景下的应用。传统的聚类方法（例如K-均值）通常依赖于静态距离指标，例如欧几里得距离，这些指标不足以捕获财务回报固有的时间依赖性。通过将动态时间翘曲（DTW）纳入距离度量标准，TS-K均值解决了这一限制，从而提高了与时间相关的财务数据中聚类的鲁棒性。这项研究通过整合TS-K均值来扩展添加噪声模型混合模型（ANM-MM）框架，从而促进了更准确的因果推理和机理聚类。该方法通过模拟验证并应用于现实世界的财务数据，证明了其在增强复杂财务时间序列分析的有效性，尤其是在确定基于基本生成机制的因果关系和聚类数据方面。结果表明，TS-K-Means的表现优于传统的K-均值，尤其是在较小的数据集中，同时随着数据集大小的变化而保持强大的因果方向检测。
This paper investigates the application of Time Series K-means (TS-K-means) within the context of causal inference and mechanism clustering of financial time series data. Traditional clustering approaches like K-means often rely on static distance metrics, such as Euclidean distance, which inadequately capture the temporal dependencies intrinsic to financial returns. By incorporating Dynamic Time Warping (DTW) as a distance metric, TS-K-means addresses this limitation, improving the robustness of clustering in time-dependent financial data. This study extends the Additive Noise Model Mixture Model (ANM-MM) framework by integrating TS-K-means, facilitating more accurate causal inference and mechanism clustering. The approach is validated through simulations and applied to real-world financial data, demonstrating its effectiveness in enhancing the analysis of complex financial time series, particularly in identifying causal relationships and clustering data based on underlying generative mechanisms. The results show that TS-K-means outperforms traditional K-means, especially with smaller datasets, while maintaining robust causal direction detection as the dataset size changes.
