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

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

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...

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Bibliographic Details
Main Authors: Tariku Tesfaye Haile, Fenglin Tian, Ghada AlNemer, Boping Tian
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Mathematics
Subjects:
ARIMA
Kalman smoothing
time-average variance constant
robust estimation
moving sum
wild binary segmentation
Online Access:https://www.mdpi.com/2227-7390/12/20/3189
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