通过分块验证提升时间序列因果发现的稳定性
VCDF: A Validated Consensus-Driven Framework for Time Series Causal Discovery
- 在不改动原有算法的前提下，通过分块评估因果关系稳定性来增强鲁棒性
- 在多种数据条件下，使VAR-LiNGAM的F1得分提升0.08至0.12
- 特别适合长序列数据，长度超1000时可提升0.18，适用于真实噪声场景
时间序列因果发现对理解动态系统至关重要，但现有方法易受噪声、非平稳性和采样变异性影响。我们提出验证共识驱动框架（VCDF），一种简单且与方法无关的增强层，通过在分块时间子集上评估因果关系的稳定性来提升鲁棒性。该框架无需修改基础算法，可兼容VAR-LiNGAM和PCMCI等方法。在合成数据上的实验表明，VCDF在不同数据特征下使VAR-LiNGAM的窗口和总结F1得分提升约0.08–0.12，尤其在中长序列中表现更优；序列长度达1000以上时，绝对提升最高可达0.18。在模拟fMRI数据和IT监控场景中的评估进一步证明其在真实噪声条件下具备更强的稳定性和结构准确性。VCDF为时间序列因果发现提供了无需改变建模假设的有效可靠性层。
原文摘要 · Abstract (English)
Time series causal discovery is essential for understanding dynamic systems, yet many existing methods remain sensitive to noise, non-stationarity, and sampling variability. We propose the Validated Consensus-Driven Framework (VCDF), a simple and method-agnostic layer that improves robustness by evaluating the stability of causal relations across blocked temporal subsets. VCDF requires no modification to base algorithms and can be applied to methods such as VAR-LiNGAM and PCMCI. Experiments on synthetic datasets show that VCDF improves VAR-LiNGAM by approximately 0.08-0.12 in both window and summary F1 scores across diverse data characteristics, with gains most pronounced for moderate-to-long sequences. The framework also benefits from longer sequences, yielding up to 0.18 absolute improvement on time series of length 1000 and above. Evaluations on simulated fMRI data and IT-monitoring scenarios further demonstrate enhanced stability and structural accuracy under realistic noise conditions. VCDF provides an effective reliability layer for time series causal discovery without altering underlying modeling assumptions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。