Data-adaptive structural change-point detection via isolation

TL;DR

DAIS is a novel, data-adaptive change-point detection method that accurately estimates the number and location of change-points in various signal structures with reduced computational complexity.

Contribution

Introduces DAIS, a new data-adaptive algorithm for change-point detection that improves accuracy and efficiency over existing methods.

Findings

DAIS achieves comparable or better accuracy than state-of-the-art methods.

DAIS significantly reduces computational complexity.

Effective on both univariate and multivariate signals.

Abstract

In this paper, a new data-adaptive method, called DAIS (Data Adaptive ISolation), is introduced for the estimation of the number and the location of change-points in a given data sequence. The proposed method can detect changes in various different signal structures; we focus on the examples of piecewise-constant and continuous, piecewise-linear signals. The novelty of the proposed algorithm comes from the data-adaptive nature of the methodology. At each step, and for the data under consideration, we search for the most prominent change-point in a targeted neighborhood of the data sequence that contains this change-point with high probability. Using a suitably chosen contrast function, the change-point will then get detected after being isolated in an interval. The isolation feature enhances estimation accuracy, while the data-adaptive nature of DAIS is advantageous regarding, mainly, computational complexity. The methodology can be applied to both univariate and multivariate signals. The simulation results presented indicate that DAIS is at least as accurate as state-of-the-art competitors and in many cases significantly less computationally expensive.