Sunday, November 21, 2021

The Change Points Detection Perfomalist API beta version is released. Everybody is welcome to test!

Link to tool:

Control Points API


'Accept: text/plain'
'Content-Type: text/csv'


Post body should be input data in CSV format. First three lines are parameters also in CSV format.

  • sValue - Statistical band in %, where 100 is UCL=MAX, 0 is UCL=LCL=mean).
  • eValue - Exception Value (EV) threshold in % of actual historical average.
  • BaseLineLength - The time period to compare current value against.
For example:
sValue, 99
eValue, 5
BaseLineLength , 7

These may be omitted in which case default values will be used.

Parameters are followed by data as shown in example input which could downloaded from

Date, Hour, Value 


Input data should be provided as a body of the API call.


Output is JSON style data:

    "Change Point": {            #full list of values for respective dates, populated by zeroes if no change point detected to aid with graphing
         "Date": value
    "Change Points Only": {      #only dates of change points with respective values
        "Change Point": {
            "Date": value
    "Ev": {                      #exeption values for respective dates
        "Date": value
    "LCL": {                     #lower control limit value for respective dates
        "Date": value
    "Moving Average": {          #moving average value for respective dates
        "Date": value
    "UCL": {                     #upper control limit value for respective dates
        "Date": value
    "Value": {                   #user input value for respective dates
        "Date": value
EXAMPLE 1 is applied against the sample data from by
using Postman tool:

After copying data to a spreadsheet, the control points could be validated visually:

EXAMLE 2: With a a some step jump event to detect:
Original Change Point Detection method explained here:

The next step is to build Perfomalist CPD UI.

Perfomalist team is presenting at international conference in Orlando.

PRODUCT: LinkedIn Post ABSTRACT: The MASF/SETDS method of detecting changes and anomalies in performa...