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Change point detection becomes more and more important as datasets increasein size, where unsupervised detection algorithms can help users process data.To detect change points, a number of unsupervised algorithms have beendeveloped which are based on different principles. One approach is to define anoptimisation problem and minimise a cost function along with a penaltyfunction. In the optimisation approach, the choice of the cost function affectsthe predictions made by the algorithm. In extension to the existing studies, anew type of cost function using Tikhonov regularisation is introduced. Anotherapproach uses Bayesian statistics to calculate the posterior probabilitydistribution of a specific point being a change point. It uses a prioriknowledge on the distance between consecutive change points and a likelihoodfunction with information about the segments. The optimisation and Bayesianapproaches for offline change point detection are studied and applied tosimulated datasets as well as a real world multi-phase dataset. The approacheshave previously been studied separately and a novelty lies in comparing thepredictions made by the two approaches in a specific setting, consisting ofsimulated datasets and a real world example. The study has found that theperformance of the change point detection algorithms are affected by thefeatures in the data.

Interactive Change Point Detection using optimisation approach and Bayesian statistics applied to real world applications