Detecting Possibly Non-Consecutive Outliers in Industrial Time Series
工业时间序列中可能不连续出现的异常值检测
Summary A method for robust estimation and multiple outlier detection in time series generated by autoregressive integrated moving average processes in industrial environments is developed. The procedure is based on reweighted maximum likelihood estimation using Huber or redescending weights and, therefore, generalizes the well-established robust M-estimation procedures used in the regression framework. When the scalar process is non-stationary, the computations required can…