ChangePointDetectionRT Function | Teradata Vantage - ChangePointDetectionRT (ML Engine) - Teradata Vantage

Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
9.02
9.01
2.0
1.3
Published
February 2022
Language
English (United States)
Last Update
2022-02-10
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dita:id
B700-4003
lifecycle
previous
Product Category
Teradata Vantage™

Real-time change-point detection typically uses the sliding window algorithm and assumes that the data follows a distribution, but has different parameters θ in different segments. In the two following hypotheses about the parameter,

H0:θ = θ0

H1:θ = θ1

the reference part has parameter θ0 and the testing part in the sliding window has parameter θ1, with the following notation:


Notation used by Machine Learning Engine function ChangePointDetectionRT

This is the decision rule for testing the sliding window:

If Sjk < h0, choose H0.

If Sjkh0, choose H1.

The ChangePointDetectionRT function detects change points in a stochastic process or time series, using real-time change-point detection, implemented with these algorithms:

  • Search algorithm: sliding window
  • Segmentation algorithm: normal distribution

Use this function when the input data cannot be stored in memory, or when the application requires a real-time response. If the input data can be stored in memory and the application does not require a real-time response, use the function ChangePointDetection (ML Engine).