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In recent years, there has been an increasing demand on efficient algorithmsfor large scale change point detection problems. To this end, we propose seededbinary segmentation, an approach relying on a deterministic construction ofbackground intervals, called seeded intervals, in which single change pointsare searched. The final selection of change points based on the candidates fromseeded intervals can be done in various ways, adapted to the problem at hand.Thus, seeded binary segmentation is easy to adapt to a wide range of changepoint detection problems, let that be univariate, multivariate or evenhigh-dimensional.  We consider the univariate Gaussian change in mean setup in detail. For thisspecific case we show that seeded binary segmentation leads to a near-lineartime approach (i.e. linear up to a logarithmic factor) independent of theunderlying number of change points. Furthermore, using appropriate selectionmethods, the methodology is shown to be asymptotically minimax optimal. Whilecomputationally more efficient, the finite sample estimation performanceremains competitive compared to state of the art procedures. Moreover, weillustrate the methodology for high-dimensional settings with an inversecovariance change point detection problem where our proposal leads to massivecomputational gains while still exhibiting good statistical performance.

Seeded Binary Segmentation: A general methodology for fast and optimal change point detection