Research on outlier detection algorithm of XmR control chart

A novel outlier detection algorithm was proposed based on the XmR control chart to address the complicated calculation and its time-consuming method in detecting isolated forest anomalies. By calculating the single-valued mean, its moving range and average of the sample attributes, we can draw the control limits and centerlines of the X and mR charts, and the single-valued attributes of the samples in the chart. According to the points in the X chart that exceeds the limits Sample number, add 1 to the sample number corresponding to the point that exceeds the limit in the mR graph, we take the union and delete it from the data, and then replace them after the deletion of the anomaly point with the CART. We use the random forest and support vector machine algorithm for experimental validations. The results show that this method has a faster speed and better precisions compared with the isolation forest method, which provides a new research idea for outlier detection.