The "Exercise 5. Build your weekly IT-Control Chart by Perfomalist tool" was added to my on-line CMG class "Perfomaly Detection"

As more trainees keep enrolling to my CMG/Teachable on-line class, (https://cmg1.teachable.com/p/perfomaly_detection) I have started updating the content.

So far I have added an additional exercise to use recently developed free performance profiler web tool "https://www.perfomalist.com/". So the trainees can build the weekly IT-Control Chart with the provided sample data and with their own data.

The exercise is open for every one here: https://cmg1.teachable.com/courses/perfomaly_detection/lectures/17731726 

Perfomalist tool users activity report

From Google Analytics

#AnomalyDetection Free Web App "PERFOMALIST" (v1.0) is online and ready for beta testing

Welcome to PERFOMALIST v1.0 - date-time-stamped data on-line analyser  https://www.perfomalist.com/

Functionality: 

1. VISUALIZATION. Weekly (Monthly in v2+) data profiling to visualize patterns, anomalies and short term seasonality via IT-Control Charts. (v1.0)

2. ANALYSIS. Anomalies and Change Points Detection in date-time stamped data. (v2+)

Method: SPC/MASF based Statistical Exception and Trend Detection (SETDS).

INPUT: CSV file with timestamp data (time series observations of a dynamic object). 

Sample input data can be downloaded from from the Download Input Data Sample (https://www.perfomalist.com/sample-upload.csv)

which should look like:

Data granularity: hourly (v1.0); minutely, daily (v2+)

OUTPUT:

- IT-Control Chart (see example below)  (v1.0)

- Data cube with  summarized data (168 rows/weekhours - v1.0)

- List of anomalies and change points (v2+)

Requirement: 

Input data should consist of at least 3 weeks of history as the method requires comparing the last 7 days of data (actual) with at least 2 weeks long learning/reference data set (baseline). 

The size of the history is limited by about 5 years. Unlimited size of input data will be implemented in v.2.

Additional resources:

- How to read IT-Control Chart (on-line article)

- On-line class "Performance Anomaly Detection"

Project contributors:
- Anfisa Trubina