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Wednesday, April 18, 2018

Performance #AnomalyDetection Online Course Info and #Perfomalist Tool DEMO


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)

"Perfomaly"=Performance Anomaly:

Control Chart as a Machine Learning Tool to Detect Anomalies

Tuesday, April 17, 2018

Perfomalist is a #ChangeDetection system based on Exception Value (EV) data analysis

2022 update:
Now the same method is used for free online tool - Perfomalist https://www.perfomalist.com/
More detailed has been added to the method and published here "

CPD - Change Point Detection is planed to be implemented in the free web tool Perfomalist


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

The second part of SETDS - trend detection recently implemented on R and got the "TrendieR" name - is actually a Change Point 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)


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: