标题： 自回归时间序列的高效因果发现 显示英文标题

标题： Efficient Causal Discovery for Autoregressive Time Series

摘要： 在本研究中，我们提出了一种新的基于约束的算法，用于因果结构学习，该算法专门针对非线性自回归时间序列设计。 与现有方法相比，我们的算法显著降低了计算复杂度，使其在处理更大问题时更加高效和可扩展。 我们对合成数据集上的性能进行了严格评估，结果表明，我们的算法不仅优于现有技术，而且在数据可用性有限的情况下也表现出色。 这些结果突显了其在需要从非线性时间序列数据中进行高效且准确因果推断的领域中的应用潜力。 显示英文摘要

摘要： In this study, we present a novel constraint-based algorithm for causal structure learning specifically designed for nonlinear autoregressive time series. Our algorithm significantly reduces computational complexity compared to existing methods, making it more efficient and scalable to larger problems. We rigorously evaluate its performance on synthetic datasets, demonstrating that our algorithm not only outperforms current techniques, but also excels in scenarios with limited data availability. These results highlight its potential for practical applications in fields requiring efficient and accurate causal inference from nonlinear time series data.