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:
This is the decision rule for testing the sliding window:
If Sjk < h0, choose H0.
If Sjk ≥ h0, 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).