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Computationally Efficient Algorithms for Detecting Changepoints

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If you have a question about this talk, please contact Mustapha Amrani.

Inference for Change-Point and Related Processes

We consider algorithms that can obtained the optimal segmentation of data under approaches such as penalised likelihood. The penalised likelihood criteria requires the user to specify a penalty value, and the choice of penalty will affect the number of changepoints that are detected. We show how it is possible to obtain the optimal segmentation for all penalty values across a continuous range. The computational complexity of this approach can linear in the number of data points, and linear in the difference in the number of changepoints between the optimal segmentations for the smallest and largest penalty values. The algorithm can be used to find optimal segmentations under the minimum description length criteria in a much more efficient manner than using the segment neighbourhood algorithm.

This talk is part of the Isaac Newton Institute Seminar Series series.

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