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Change point detection via feedforward neural networks with theoretical guarantees

Author

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  • Zhou, Houlin
  • Zhu, Hanbing
  • Wang, Xuejun

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.

Suggested Citation

  • Zhou, Houlin & Zhu, Hanbing & Wang, Xuejun, 2024. "Change point detection via feedforward neural networks with theoretical guarantees," Computational Statistics & Data Analysis, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:csdana:v:193:y:2024:i:c:s0167947323002244
    DOI: 10.1016/j.csda.2023.107913
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    References listed on IDEAS

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