Text

Bayesian Hierarchical Model for Change Point Detection in Multivariate Sequences

Motivated by the wind turbine anomaly detection, we propose a Bayesian hierarchical model (BHM) for the mean-change detection in multivariate sequences. By combining the exchange random order distribution induced from the Poisson–Dirichlet process and nonlocal priors, BHM exhibits satisfactory performance for mean-shift detection with multivariate sequences under different error distributions. In particular, BHM yields the smallest detection error compared with other competitive methods considered in the article. We use a local scan procedure to accelerate the computation, while the anomaly locations are determined by maximizing the posterior probability through dynamic programming. We establish consistency of the estimated number and locations of the change points and conduct extensive simulations to evaluate the BHM approach. Among the popular change point detection algorithms, BHM yields the best performance for most of the datasets in terms of the F1 score for the wind turbine anomaly detection.

Ketersediaan

Informasi Detail

Judul Seri

TECHNOMETRICS; Vol.64 No.2 May 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 177-186

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MULTIVARIATE DATA
CHANGE POINTS
NONLOCAL PRIOR
NONMAXIMUM SUPPRESSION
POISSON–DIRICHLET PROCESS

Info Detail Spesifik

https://doi.org/10.1080/00401706.2021.1927848

Pernyataan Tanggungjawab

Huaqing Jin, Guosheng Yin, Binhang Yuan, Fei Jiang

Versi lain/terkait

Lampiran Berkas

Komentar