Multiscale Change Point Detection for Univariate Time Series Data with Missing Value
Multiscale Change Point Detection for Univariate Time Series Data with Missing Value
About this item
Full title
Author / Creator
Publisher
Basel: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Basel: MDPI AG
Subjects
More information
Scope and Contents
Contents
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 s...
Alternative Titles
Full title
Multiscale Change Point Detection for Univariate Time Series Data with Missing Value
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_44de3c10e40945bd8e6dc1a0f407f935
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_44de3c10e40945bd8e6dc1a0f407f935
Other Identifiers
ISSN
2227-7390
E-ISSN
2227-7390
DOI
10.3390/math12203189
