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. We highlight, however, that our algorithm can be extended to other frameworks, such as piecewise-quadratic signals. The data-adaptivity of our methodology lies in the fact that, 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 and accuracy. The simulation results presented indicate that DAIS is at least as accurate as state-of-the-art competitors.

翻译：摘要: 本文提出一种名为DAIS（Data Adaptive ISolation）的新型数据自适应方法，用于估计给定数据序列中变化点的数量与位置。该方法能够检测多种不同信号结构中的变化，本文重点针对分段常数信号和连续分段线性信号进行案例分析，同时强调该算法可扩展至其他框架（如分段二次信号）。本方法的数据自适应性体现在：每一步处理当前数据时，会在高概率包含变化点的目标邻域内搜索最显著的变化点。通过选用恰当的对比函数，该变化点可在被隔离于某区间后成功检出。隔离特性提升了估计精度，而DAIS的数据自适应优势主要体现在计算复杂度与准确率方面。仿真结果表明，DAIS在精度上至少与当前最优方法相当。