Model‐free offline change‐point detection in multidimensional time series of arbitrary nature via ϵ‐complexity: Simulations and applications

Boris Darkhovsky and Alexandra Piryatinska

Applied Stochastic Models in Business and Industry, 2018, vol. 34, issue 5, 633-644

Abstract: 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.

Date: 2018
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https://doi.org/10.1002/asmb.2303

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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:34:y:2018:i:5:p:633-644

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