PMQ

Trend Forecasting Monitor

For sepecific device or sensor data(HDD Health, System CPU Usage and so on), RMM offers prediction function(Trend Forecasting Monitor). It can help users know abnormal data status as soon as possible to prevent some damages.Predictive Tyoe of Trend Forecasting Monitor are hourly averages of data, and they contain averages of current hour and even next 3 hours. 

Predictive Process:

The first condiction is predictive object has at least the newest 48 history hourly averages, and these samples are calculated by time-series prediction algorithm. Time-series prediction algorithm RMM adopts is Exponential Smoothing, and is based on APIs in Weka machinne learning java libary. By the above Process, it can produce values of prediction, and Trend Forecasting Monitor takes these values and the newest 3 history hourly averages to compare with hourly averages in yesterday. If there are significant differences between values of prediction and hourly averages in yesterday, the status of analytic result is "Potential Risk". If there are significant differences between the newest 3 history hourly averages and hourly averages in yesterday, the status is "Doubtful Risk". The standard of significant differences is based on Tukey's Range Test, and the formula is: [Q₁-3(Q₃-Q₁), Q3+3(Q₃-Q₁)].Q₁ and Q₃ are the lower and upper quartile of hourly averages in yesterday. If objects are not in the above range, there are significant differences between them.