提出快速因果发现算法，让大规模时间序列分析在电脑上跑得更快。
Scalable Time-Series Causal Discovery with Approximate Causal Ordering
- 用一次性计算替代反复迭代，大幅降低计算开销。
- 400变量金融数据下比标准方法快7-13倍，比GPU版快4.5倍。
- 适合需要快速分析海量时间序列因果关系的研究者使用。
时间序列因果发现面临显著计算挑战，传统算法在变量或样本量大时效率极低。本文提出一种变体LiNGAM算法的启发式近似方法，通过省略迭代优化步骤，实现所有统计值的一次性预计算。该改进将时间复杂度从 $O(m^3n)$ 降至 $O(m^2n + m^3)$，空间复杂度保持 $O(m^2)$，其中 $m$ 为变量数，$n$ 为样本数。尽管是近似方法，仍保留原算法的核心结构与实证可靠性。在含最多400变量的大型金融数据上，新算法相比标准实现提速7–13倍，比GPU加速版本快4.5倍。在医疗影像、服务器监控和金融等多个场景验证中表现出鲁棒性与实用可扩展性。本工作实现了计算效率与发现质量的可靠平衡，使个人计算机即可开展大规模因果分析。
原文摘要 · Abstract (English)
Causal discovery in time-series data presents a significant computational challenge. Standard algorithms are often prohibitively expensive for datasets with many variables or samples. This study introduces and validates a heuristic approximation of the VarLiNGAM algorithm to address this scalability problem. The standard VarLiNGAM method relies on an iterative search, recalculating statistical dependencies after each step. Our heuristic modifies this procedure by omitting the iterative refinement. This change permits a one-time precomputation of all necessary statistical values. The algorithmic modification reduces the time complexity from $O(m^3n)$ to $O(m^2n + m^3)$ while keeping the space complexity at $O(m^2)$, where $m$ is the number of variables and $n$ is the number of samples. While an approximation, our approach retains VarLiNGAM's essential structure and empirical reliability. On large-scale financial data with up to 400 variables, our algorithm achieves a 7--13x speedup over the standard implementation and a 4.5x speedup over a GPU-accelerated version. Evaluations across medical imaging, web server monitoring, and finance demonstrate the heuristic's robustness and practical scalability. This work offers a validated balance between computational efficiency and discovery quality, making large-scale causal analysis feasible on personal computers.
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