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Retrospective Change-Points Detection for Multidimensional Time Series of Arbitrary Nature: Model-Free Technology
Alexandra Piryatinska1, Boris Darkhovsky2
1Department of Mathematics, San Francisco State University, 1600 Holloway Ave., San Francisco, CA 94132, USA.
Abstract:
We consider a retrospective change-point detection problem for multidimensional time series of arbitrary nature (in particular, panel data). Change-points are the moments at which the changes in generating mechanism occur. Our method is based on the new theory of ϵ-complexity of individual continuous vector functions and is model-free. We present simulation results confirming the effectiveness of the method.
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