熵因果图用于多元时间序列异常检测
Entropy Causal Graphs for Multivariate Time Series Anomaly Detection
AI总结：
针对现有多元时间序列异常检测忽略变量间因果关系的问题，提出基于传递熵构建因果图并结合加权图卷积与因果卷积的CGAD框架，在真实数据集上平均提升9%。
摘要：
许多多元时间序列异常检测框架已被提出并广泛应用。然而，这些框架大多不考虑多元时间序列数据中变量之间的内在关系，从而忽略了变量间的因果关系，导致异常检测性能下降。本工作提出了一种名为CGAD的新框架，即用于多元时间序列异常检测的熵因果图。CGAD利用传递熵构建图结构，以揭示时间序列数据中潜在的因果关系。采用加权图卷积网络结合因果卷积来建模多元时间序列数据中的因果图结构和时间模式。此外，CGAD应用异常评分，利用基于中位数绝对偏差的归一化来提高异常识别过程的鲁棒性。大量实验表明，在真实世界数据集上，CGAD在三个不同的多元时间序列异常检测指标上平均提升9%，优于最先进的方法。
英文摘要：
Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between variables in multivariate time series data, thus ignoring the causal relationship among variables and degrading anomaly detection performance. This work proposes a novel framework called CGAD, an entropy Causal Graph for multivariate time series Anomaly Detection. CGAD utilizes transfer entropy to construct graph structures that unveil the underlying causal relationships among time series data. Weighted graph convolutional networks combined with causal convolutions are employed to model both the causal graph structures and the temporal patterns within multivariate time series data. Furthermore, CGAD applies anomaly scoring, leveraging median absolute deviation-based normalization to improve the robustness of the anomaly identification process. Extensive experiments demonstrate that CGAD outperforms state-of-the-art methods on real-world datasets with a 9% average improvement in terms of three different multivariate time series anomaly detection metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
