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Model‐free offline change‐point detection in multidimensional time series of arbitrary nature via ϵ ‐complexity: Simulations and applications
Author(s) -
Darkhovsky Boris,
Piryatinska Alexandra
Publication year - 2018
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2303
Subject(s) - series (stratigraphy) , computer science , change detection , algorithm , point (geometry) , time series , mathematics , artificial intelligence , machine learning , paleontology , geometry , biology
A novel method for offline detection of multiple change points in multidimensional time series is proposed. It is based on the notion of ε ‐complexity of continuous vector functions. The proposed methodology does not use any prior information on data‐generating mechanisms; therefore, it can be applied to multidimensional time series of arbitrary nature. Its performance is demonstrated in simulations and an application to high‐frequency financial data.