基于标称性分数条件化的点/序列重建时间序列异常检测
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction
AI总结：
提出一种利用基于点和序列重建模型的无监督时间序列异常检测框架，通过整合标称性分数与异常分数推导出更优的诱导异常分数，在多个公共数据集上优于大多数最先进基线方法。
摘要：
由于可能出现的模式的复杂性和多样性，时间序列异常检测具有挑战性。一个主要困难在于对时间依赖关系进行建模以发现上下文异常，同时保持对点异常的检测准确率。在本文中，我们提出了一个利用基于点和基于序列的重建模型的无监督时间序列异常检测框架。基于点的模型尝试量化点异常，基于序列的模型尝试量化点异常和上下文异常。在观测时间点是从标称时间点两阶段偏离的值的公式化下，我们引入了一个根据重建误差组合值的比率计算出的标称性分数。我们通过进一步整合标称性分数和异常分数推导出诱导异常分数，然后在理论上证明了在某些条件下诱导异常分数优于原始异常分数。在几个公共数据集上进行的大量研究表明，所提出的框架在时间序列异常检测方面优于大多数最先进的基线方法。
英文摘要：
Time series anomaly detection is challenging due to the complexity and variety of patterns that can occur. One major difficulty arises from modeling time-dependent relationships to find contextual anomalies while maintaining detection accuracy for point anomalies. In this paper, we propose a framework for unsupervised time series anomaly detection that utilizes point-based and sequence-based reconstruction models. The point-based model attempts to quantify point anomalies, and the sequence-based model attempts to quantify both point and contextual anomalies. Under the formulation that the observed time point is a two-stage deviated value from a nominal time point, we introduce a nominality score calculated from the ratio of a combined value of the reconstruction errors. We derive an induced anomaly score by further integrating the nominality score and anomaly score, then theoretically prove the superiority of the induced anomaly score over the original anomaly score under certain conditions. Extensive studies conducted on several public datasets show that the proposed framework outperforms most state-of-the-art baselines for time series anomaly detection.
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。
