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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.… Click to show full 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.
Keywords:
time series;
retrospective change;
change;
series arbitrary;
arbitrary nature;
multidimensional time
Journal Title: Entropy
Year Published: 2021
Link to full text (if available)
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Journal Title: Entropy
Year Published: 2021
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!