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

Link to tool: www.Perfomalist.com

Control Points API

POST https://api.perfomalist.com/api/controlpoints.py

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

Input

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 www.Perfomalist.com. 

Date, Hour, Value 

7/2/2011,0,236274 

7/2/2011,1,215359 

7/2/2011,2,170011

....

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

Output

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 www.Performalist.com 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:
http://www.trub.in/2020/08/cpd-change-points-detection-is-planed.html

The next step is to build Perfomalist CPD UI.