Dynamic outlier detection for process control time series
过程控制时间序列中异常值的动态检测

To improve the traditional outlier-detection by using wavelet analysis method and to deal with the instability characteristic of data from regulatory control process, we propose an improved outlier-detection method. This method combines an improved robust auto-regression (AR) model with the wavelet analysis method to eliminate the deficiency of the wavelet method in outlier-detection. To avoid the requirement of a pre-selected threshold value in the traditional method, we introduce the hidden Markov model (HMM) which analyzes the wavelet coefficients and updates online the coefficient values to improve the detection precision. Experiments and applications show that this method is especially suitable to oscillatory data in control processes.