基于信号变化速率的时间序列异常值检测方法

    Outlier Detection Method for Time Series Based on the Rate of Signal Change

    • 摘要: 为了消除奇异值对系统模型辨识的影响, 并提高时间序列数据预处理的效果, 提出一种基于统计分析的奇异值检测方法.该方法将时间序列信号变化特征与统计学理论相结合, 在计算时间序列信号的变化速率的基础上, 对其进行统计分析, 进而得到异常值发生的位置, 并利用内插法对原始的观察信号进行修复.应用结果表明:该算法简单、有效、计算量小, 能满足时间序列数据预处理的需求.

       

      Abstract: To eliminate the influence of outlier for system identification and improve the effect of data preprocessing in time serials, a simple and fast outlier detection and recovering algorithm was presented in this paper. The proposed algorithm, which included the variation rate of signal calculation, statistical parameters estimation, the position of outlier identification, and signal recovering based on interpolation, combined the “rate of signal variation”and statistic theory.Resultsshow that the proposed algorithm has the benefits of simpleness, effectiveness and low computational cost and can be satisfied for the demand of data preprocessing in time series.

       

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