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Probabilistic distance based abnormal pattern detection in uncertain series data

机译：不确定序列数据中基于概率距离的异常模式检测

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摘要

Abnormal pattern detection is an important task in series data anomaly detection. Because of the noise interference, the accuracy of abnormal detection method based on deterministic value is decreased. Whereas, most recent studies aimed at solving the anomaly detection problem in uncertain series use possible world models to describe the uncertainty in discrete data and select outliers as the anomaly detection objects. Abnormal pattern detection problems in continuous uncertain data are rarely reported. In order to improve the accuracy of abnormal pattern detection for uncertain data, we propose a Probabilistic Distance based approach for mining Abnormal Pattern Detection from uncertain series data (PD_ APD). Our considered approach re-express the Euclidean distance according to data's probability density function (PDF), and get a probabilistic metric to compute the dissimilarity of two uncertain series. Our experiments show that, compared with Tarzan, a deterministic approach that directly processes data without considering uncertainty, PD_ APD provides a flexible trade-off between false alarms and miss ratios by controlling a probabilistic abnormality threshold. Especially, when data uncertain variance is large, PD_ APD has lower false alarms under the same specific miss ratio.
机译：模式异常检测是串行数据异常检测中的重要任务。由于存在噪声干扰，因此降低了基于确定性值的异常检测方法的准确性。鉴于，旨在解决不确定序列中异常检测问题的最新研究使用可能的世界模型来描述离散数据中的不确定性，并选择异常值作为异常检测对象。连续不确定数据中异常模式检测问题很少被报道。为了提高针对不确定数据的异常模式检测的准确性，我们提出了一种基于概率距离的方法，用于从不确定序列数据（PD_ APD）中挖掘异常模式检测。我们考虑的方法根据数据的概率密度函数（PDF）重新表达了欧氏距离，并获得了一个概率度量来计算两个不确定序列的相异性。我们的实验表明，与直接处理数据而不考虑不确定性的确定性方法Tarzan相比，PD_ APD通过控制概率异常阈值提供了错误警报和未命中率之间的灵活权衡。特别是，当数据不确定方差较大时，在相同的特定未命中率下，PD_ APD的误报率较低。

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