Trimmed fuzzy clustering of financial time series based on dynamic time warping

In finance, cluster analysis is a tool particularly useful for classifying stock market multivariate time series data related to daily returns, volatility daily stocks returns, commodity prices, volume trading, index, enhanced index tracking portfolio, and so on. In the literature, following differe...

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Veröffentlicht in:	Annals of operations research Jg. 299; H. 1-2; S. 1379 - 1395
Hauptverfasser:	D’Urso, Pierpaolo, De Giovanni, Livia, Massari, Riccardo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.04.2021 Springer Springer Nature B.V
Schlagworte:	Analysis Business and Management Cluster analysis Clustering Combinatorics Data analysis Fuzzy algorithms Fuzzy logic Fuzzy systems Identification and classification Management Monte Carlo simulation Multivariate analysis Operations research Operations Research/Decision Theory Outliers (statistics) Pricing Regression analysis S.I.: Recent Developments in Financial Modeling and Risk Management Stock exchanges Stock markets Stocks Technology application Theory of Computation Time dilatation Time series Time-series analysis Volatility Warping Italy Multivariate financial time series Robust fuzzy C-medoids clustering FTSE MIB index Dynamic time warping Trimming
ISSN:	0254-5330, 1572-9338
Online-Zugang:	Volltext
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Abstract	In finance, cluster analysis is a tool particularly useful for classifying stock market multivariate time series data related to daily returns, volatility daily stocks returns, commodity prices, volume trading, index, enhanced index tracking portfolio, and so on. In the literature, following different methodological approaches, several clustering methods have been proposed for clustering multivariate time series. In this paper by adopting a fuzzy approach and using the Partitioning Around Medoids strategy, we suggest to cluster multivariate financial time series by considering the dynamic time warping distance. In particular, we proposed a robust clustering method capable to neutralize the negative effects of possible outliers in the clustering process. The clustering method achieves its robustness by adopting a suitable trimming procedure to identify multivariate financial time series more distant from the bulk of data. The proposed clustering method is applied to the stocks composing the FTSE MIB index to identify common time patterns and possible outliers.
AbstractList	In finance, cluster analysis is a tool particularly useful for classifying stock market multivariate time series data related to daily returns, volatility daily stocks returns, commodity prices, volume trading, index, enhanced index tracking portfolio, and so on. In the literature, following different methodological approaches, several clustering methods have been proposed for clustering multivariate time series. In this paper by adopting a fuzzy approach and using the Partitioning Around Medoids strategy, we suggest to cluster multivariate financial time series by considering the dynamic time warping distance. In particular, we proposed a robust clustering method capable to neutralize the negative effects of possible outliers in the clustering process. The clustering method achieves its robustness by adopting a suitable trimming procedure to identify multivariate financial time series more distant from the bulk of data. The proposed clustering method is applied to the stocks composing the FTSE MIB index to identify common time patterns and possible outliers.
Audience	Academic
Author	Massari, Riccardo D’Urso, Pierpaolo De Giovanni, Livia
Author_xml	– sequence: 1 givenname: Pierpaolo surname: D’Urso fullname: D’Urso, Pierpaolo organization: Department of Social Sciences and Economics, Sapienza - University of Rome – sequence: 2 givenname: Livia surname: De Giovanni fullname: De Giovanni, Livia organization: Department of Political Sciences, LUISS Guido Carli – sequence: 3 givenname: Riccardo surname: Massari fullname: Massari, Riccardo email: riccardo.massari@uniroma1.it organization: Department of Social Sciences and Economics, Sapienza - University of Rome
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Copyright	Springer Science+Business Media, LLC, part of Springer Nature 2019 COPYRIGHT 2021 Springer Annals of Operations Research is a copyright of Springer, (2019). All Rights Reserved. Springer Science+Business Media, LLC, part of Springer Nature 2019.
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Keywords	Multivariate financial time series Robust fuzzy C-medoids clustering FTSE MIB index Dynamic time warping Trimming
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SubjectTerms	Analysis Business and Management Cluster analysis Clustering Combinatorics Data analysis Fuzzy algorithms Fuzzy logic Fuzzy systems Identification and classification Management Monte Carlo simulation Multivariate analysis Operations research Operations Research/Decision Theory Outliers (statistics) Pricing Regression analysis S.I.: Recent Developments in Financial Modeling and Risk Management Stock exchanges Stock markets Stocks Technology application Theory of Computation Time dilatation Time series Time-series analysis Volatility Warping
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Title	Trimmed fuzzy clustering of financial time series based on dynamic time warping
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