Finite Sample Complexity Analysis of Binary Segmentation

TL;DR

This paper analyzes the finite sample complexity of binary segmentation, providing algorithms for worst and best case scenarios, synthetic data for testing, and empirical evidence of practical efficiency.

Contribution

It introduces new methods to analyze and test the complexity of binary segmentation, including algorithms and synthetic data for worst and best case scenarios.

Findings

Binary segmentation often operates near optimal speed in practice.

Algorithms for computing worst and best case splits are proposed.

Synthetic data can effectively test algorithm implementation.

Abstract

Binary segmentation is the classic greedy algorithm which recursively splits a sequential data set by optimizing some loss or likelihood function. Binary segmentation is widely used for changepoint detection in data sets measured over space or time, and as a sub-routine for decision tree learning. In theory it should be extremely fast for $N$ data and $K$ splits, $O(N K)$ in the worst case, and $O(N \log K)$ in the best case. In this paper we describe new methods for analyzing the time and space complexity of binary segmentation for a given finite $N$, $K$, and minimum segment length parameter. First, we describe algorithms that can be used to compute the best and worst case number of splits the algorithm must consider. Second, we describe synthetic data that achieve the best and worst case and which can be used to test for correct implementation of the algorithm. Finally, we provide an empirical analysis of real data which suggests that binary segmentation is often close to optimal speed in practice.