One approach was using the Moving Average (M.A.) Filter was that the optimum size of the window for good FPRs and DRs was data dependent. In case of noisy data like in Figure 1, the results didn’t change much with varying size of the filter. But in data like shown in Figure 2, which include periodicity and trendy variations (an example would be wind blowing in a storm with varying speeds), the FPRs and DRs changed considerably with size of M.A. Filter. Moreover the outlierness of the outliers decreases if larger size windows are used. E.g. if a window size of 25 is used and after 20 normal readings, if 4 or 5 outliers come, the smoothed will value less. The reasons why M.A. filter was not pursued are thus summed up below: The window size is a significant factor which is user defined parameter and gives different results for different readings. The outlier-ness of the outliers is decreased which is bound to affect Detection Rates. Variations like shown in Figure 1 are usually very low compared to the useful data and filters are already implemented in the hardware at DSP level to average/filter such data. Noise as shown in Figure 1 is usually concentrated near mean/center of ellipsoids and due to this reason and the one mentioned in point 3 above such reading are seldom detected as False Positives. The aim was to bind the data variations as tightly as possible by the hyper-ellipsoid without affecting of the outliers. This will lead to minimization of False “outlier-ness” Positive Rates and increase in DRs. The algorithm developed resulted in excellent results boasting not only low False Positive Rates (FPR) but, at the same time, approaching 100 % Detection Rates (DR). Smoothing
One approach was using the Moving Average (M.A.) Filter was that the optimum size of the window for good FPRs and DRs was data dependent. In case of noisy data like in Figure 1, the results didn’t change much with varying size of the filter. But in data like shown in Figure 2, which include periodicity and trendy variations (an example would be wind blowing in a storm with varying speeds), the FPRs and DRs changed considerably with size of M.A. Filter. Moreover the outlierness of the outliers decreases if larger size windows are used. E.g. if a window size of 25 is used and after 20 normal readings, if 4 or 5 outliers come, the smoothed will value less. The reasons why M.A. filter was not pursued are thus summed up below: The window size is a significant factor which is user defined parameter and gives different results for different readings. The outlier-ness of the outliers is decreased which is bound to affect Detection Rates. Variations like shown in Figure 1 are usually very low compared to the useful data and filters are already implemented in the hardware at DSP level to average/filter such data. Noise as shown in Figure 1 is usually concentrated near mean/center of ellipsoids and due to this reason and the one mentioned in point 3 above such reading are seldom detected as False Positives. The aim was to bind the data variations as tightly as possible by the hyper-ellipsoid without affecting “outlier-ness” of the outliers. This will lead to minimization of False Positive Rates and increase in DRs. The algorithm developed resulted in excellent results boasting not only low False Positive Rates (FPR) but, at the same time, approaching 100 % Detection Rates (DR). Smoothing
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