Abstract
In this work, we introduce a dual-criterion framework for change-point detection. This innovative approach intuitively facilitates the simultaneous determination of both the number and locations of change points, while effectively identifying and excluding outliers. Numerous one-dimensional criterion-based change point detection methods can be generalized and integrated within this novel framework.
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