通过捕捉时间序列的因果节律，提升异常检测精度与可解释性。
CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series
- 构建结构因果模型，解析异常生成机制。
- 在7个真实数据集上，AUROC提升3%至17%。
- 适合需要高可解释性的工业监控场景。
时间序列异常检测在多个领域受到广泛关注。现有方法往往难以捕捉异常生成背后的底层机制，且常面临标签稀缺、数据不平衡及复杂多周期性等固有挑战。本文提出基于因果关系的新框架CaPulse，通过捕捉时间序列的潜在因果脉冲实现高效异常检测。具体而言，首先建立结构因果模型以解析异常生成过程；为应对数据难题，提出带新型掩码机制的周期性归一化流与周期性学习器，构建感知周期性的密度基异常检测方法。在七个真实数据集上的大量实验表明，CaPulse持续优于现有方法，AUROC提升达3%至17%，同时具备更强可解释性。
原文摘要 · Abstract (English)
Time series anomaly detection has garnered considerable attention across diverse domains. While existing methods often fail to capture the underlying mechanisms behind anomaly generation in time series data. In addition, time series anomaly detection often faces several data-related inherent challenges, i.e., label scarcity, data imbalance, and complex multi-periodicity. In this paper, we leverage causal tools and introduce a new causality-based framework, CaPulse, which tunes in to the underlying causal pulse of time series data to effectively detect anomalies. Concretely, we begin by building a structural causal model to decipher the generation processes behind anomalies. To tackle the challenges posed by the data, we propose Periodical Normalizing Flows with a novel mask mechanism and carefully designed periodical learners, creating a periodicity-aware, density-based anomaly detection approach. Extensive experiments on seven real-world datasets demonstrate that CaPulse consistently outperforms existing methods, achieving AUROC improvements of 3% to 17%, with enhanced interpretability.
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