Change point detection via feedforward neural networks with theoretical guarantees

IF 1.5 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computational Statistics & Data Analysis Pub Date : 2024-01-09 DOI:10.1016/j.csda.2023.107913

Houlin Zhou, Hanbing Zhu, Xuejun Wang

引用次数: 0

Abstract

This article mainly studies change point detection for mean shift change point model. An estimation method is proposed to estimate the change point via feedforward neural networks. The complete f-moment consistency of the proposed estimator is obtained. Numerical simulation results show that the performance of the proposed estimator is better than that of cumulative sum type estimator which is widely used in the change point detection, especially when the mean shift signal size is small. Finally, we demonstrate the proposed method by empirically analyzing a stock data set.

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通过具有理论保证的前馈神经网络进行变化点检测

本文主要研究均值移动变化点模型的变化点检测。提出了一种通过前馈神经网络估计变化点的估计方法。得到了所提估计器的完全 F-时刻一致性。数值模拟结果表明，所提估计器的性能优于变化点检测中广泛使用的累积和式估计器，尤其是当均值偏移信号较小时。最后，我们通过对股票数据集进行实证分析，证明了所提出的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Statistics & Data Analysis 数学-计算机：跨学科应用

CiteScore

3.70

自引率

5.60%

发文量

167

审稿时长

60 days

期刊介绍： Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article. II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. [...] III) Special Applications - [...] IV) Annals of Statistical Data Science [...]