Premium
Inference for single and multiple change‐points in time series
Author(s) -
Jandhyala Venkata,
Fotopoulos Stergios,
MacNeill Ian,
Liu Pengyu
Publication year - 2013
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12035
Subject(s) - inference , series (stratigraphy) , bayes' theorem , time series , multivariate statistics , econometrics , mathematics , change detection , time point , statistics , parametric statistics , point estimation , computer science , point (geometry) , statistical inference , data mining , algorithm , bayesian probability , artificial intelligence , paleontology , philosophy , geometry , biology , aesthetics
The article reviews methods of inference for single and multiple change‐points in time series, when data are of retrospective (off‐line) type. The inferential methods reviewed for a single change‐point in time series include likelihood, Bayes, Bayes‐type and some relevant non‐parametric methods. Inference for multiple change‐points requires methods that can handle large data sets and can be implemented efficiently for estimating the number of change‐points as well as their locations. Our review in this important area focuses on some of the recent advances in this direction. Greater emphasis is placed on multivariate data while reviewing inferential methods for a single change‐point in time series. Throughout the article, more attention is paid to estimation of unknown change‐point(s) in time series, and this is especially true in the case of multiple change‐points. Some specific data sets for which change‐point modelling has been carried out in the literature are provided as illustrative examples under both single and multiple change‐point scenarios.