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An Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation

Daniel Lemire·Martin Brooks·Yuhong Yan
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Abstract

Monotonicity is a simple yet significant qualitative characteristic. We consider the problem of segmenting an array in up to K segments. We want segments to be as monotonic as possible and to alternate signs. We propose a quality metric for this problem, present an optimal linear time algorithm based on novel formalism, and compare experimentally its performance to a linear time top-down regression algorithm. We show that our algorithm is faster and more accurate. Applications include pattern recognition and qualitative modeling.

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Daniel Lemire, Martin Brooks, Yuhong Yan. 2007-02-24. An Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation. https://arxiv.org/abs/cs/0702142

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