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

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

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 applie...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied stochastic models in business and industry 2018-09, Vol.34 (5), p.633-644
Hauptverfasser: Darkhovsky, Boris, Piryatinska, Alexandra
Format: Artikel
Sprache:eng
Schlagworte:
Change detection
change‐point problem
Complexity
Computer simulation
Continuity (mathematics)
multidimensional time series
Time series
ε‐complexity
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:1524-1904
1526-4025
DOI:10.1002/asmb.2303