By subtracting the forecast sketch from the current one, we obtain the forecast error sketches.
Weighted standard deviation function in r series#
The forecast time series analysis method (e.g., EWMA, or exponentially weighted moving average) can help remove noise. Based on linearity of the sketches, we summarize the sketches over multiple routers into an aggregate sketch and apply time series analysis methods for aggregate sketches to obtain the forecast sketches for change detection. First, we record the network traffic with sketches in each router. It signifies a noticeable change in process dynamics due to major disturbance or fault is detected.ĮWMA Chart: Exponential Weighted Moving Average (EWMA) chart is a weighted plot of statistics of process variable, usually the process variable x itself or the sample mean x ¯, by placing a weight w, 0 ≤ w ≤ 1 on the most recent data point and a forgetting factor 1 – w on the last statistics.įigure 16.8 shows the architecture of the HiFIND system. If the previous points fall out of the mask, the process is said to be not in statistical control. It determines the maximum statistically allowable deviation of the previous data points. The control limit of CUSUM is expressed as an overlay mask. Due to this nature, the definition of control limits of CUSUM is not UCL and LCL. The real concern is the slope or the deviation between successive data points. Therefore, in using CUSUM charts, it is not our concern whether or not the cumulated sum of the statistics falls over a fixed UCL and LCL. The objective of using CUSUM is to detect changes in monitoring statistics.
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