Change detection tests using the ICI rule

Designing tests able to effectively detect changes in the stationarity of a process generating data is a challenging problem, in particular when the process is unknown, and the only information available has to be extracted from a set of observations. This work proposes a novel approach for detecting changes in a process generating data whose distribution is unknown. Peculiarity of the approach is the use of the Intersection of Confidence Intervals (ICI) rule to monitor the process evolution. A change detection test derived from this approach is also presented. Experimental results show that the proposed test outperforms state-of-the art solutions, both in terms of efficiency and effectiveness, in particular when a reduced test configuration set is available.

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