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Wednesday, April 18, 2018

Performance #AnomalyDetection Online Course is Launched


What is covered:

  • Machine learning based Anomaly Detection technique
  • Classical (SPC) and MASF (For system performance data) Control Chartirting
  • Where is the Control Chart Used?
  • What are the types of Control Charts?
  • Reading, building, and interpreting Control Charts
  • Typical cases of real world issues captured by anomaly detection system (VMs, Mainframes, Middleware, E2E response and more)
  • How to build free AWS cloud server with R and build there control charts
  • Performance anomaly (Perfomaly) detection system R implementation example (SEDS-lite - open source based tool)
Previews:


"Perfomaly"=Performance Anomaly:



Control Chart as a Machine Learning Tool to Detect Anomalies



Tuesday, April 17, 2018

TrendieR is a #ChangePointsDetection system based on Exception Value (EV) data analysis

The right term for SETDS methodology is Anomaly and Change (points) detection.

The second part of SETDS - trend detection recently implemented on R and got the "TrendieR" name - is actually a Change Points Detection tool.

See more details how it works in the previous post:

AnomalyDetection vs. NoveltyDetection. SETDS Method Detects and Separates both


But in short the change points are the the following equation roots (solutions)

                  EV(t)=0

Where t is time and EV is Exception Value (magnitude of exception calculated as the difference between actual data and baseline statistical limits UCL and LCL). 

CMG.org paper there was the attempt to describe that calculation more formally by the following formulas: