Change point detection via feedforward neural networks with theoretical guarantees

被引：0

作者：

Zhou, Houlin ^{[1
]}

Zhu, Hanbing ^{[1
]}

Wang, Xuejun ^{[1
]}

机构：

[1] Anhui Univ, Sch Big Data & Stat, Hefei 230601, Peoples R China

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2024年 / 193卷

基金：

中国国家自然科学基金;

关键词：

Change point detection; Complete f-moment consistency; Cumulative sum; Feedforward neural networks; CONVERGENCE; MODEL;

D O I：

10.1016/j.csda.2023.107913

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页数：17

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