Multiscale Change Point Detection for Univariate Time Series Data with Missing Value

Autor:	Tariku Tesfaye Haile, Fenglin Tian, Ghada AlNemer, Boping Tian
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	ARIMA Kalman smoothing time-average variance constant robust estimation moving sum wild binary segmentation Mathematics QA1-939
Zdroj:	Mathematics, Vol 12, Iss 20, p 3189 (2024)
Druh dokumentu:	article
ISSN:	2227-7390
DOI:	10.3390/math12203189
Popis:	This paper studies the autoregressive integrated moving average (ARIMA) state space model combined with Kalman smoothing to impute missing values in a univariate time series before detecting change points. We estimate a scale-dependent time-average variance constant that depends on the length of the data section and is robust to mean shifts under serial dependence. The consistency of the proposed estimator is shown under the assumption allowing heavy tailedness. Integrating the proposed estimator with the moving sum and wild binary segmentation procedures to determine the number and locations of change points is discussed. Furthermore, the performance of the proposed methods is evaluated through extensive simulation studies and applied to the Beijing multi-site air quality dataset to impute missing values and detect mean changes in the data.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/44de3c10e40945bd8e6dc1a0f407f935 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.