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,
H 0:θ = θ 0
H 1:θ = θ 1
the reference part has parameter θ 0 and the testing part in the sliding window has parameter θ 1, with the following notation:
This is the decision rule for testing the sliding window:
If S j k < h 0, choose H 0.
If S j k ≥ h 0, choose H 1.
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.