论文标题
双变量信号的异常检测
Anomaly Detection for Bivariate Signals
论文作者
论文摘要
单变量或多变量时间序列的异常检测问题在许多实际应用中是一个关键问题，因为工业过程控制，生物测量，发动机监测，对各种行为的监督。在本文中，我们提出了一种简单而经验的方法来检测多元时间序列行为中的异常情况。该方法基于数据的条件分位数的经验估计，该量值为置信管提供了上限和下限。该方法经过人工数据测试，其有效性已在一个真实的框架中得到了证明，例如对飞机发动机的监视。
The anomaly detection problem for univariate or multivariate time series is a critical question in many practical applications as industrial processes control, biological measures, engine monitoring, supervision of all kinds of behavior. In this paper we propose a simple and empirical approach to detect anomalies in the behavior of multivariate time series. The approach is based on the empirical estimation of the conditional quantiles of the data, which provides upper and lower bounds for the confidence tubes. The method is tested on artificial data and its effectiveness has been proven in a real framework such as that of the monitoring of aircraft engines.
