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Fuzzy Clustering Stability Evaluation of Time Series

The discovery of knowledge by analyzing time series is an important field of research. In this paper we investigate multiple multivariate time series, because we assume a higher information value than regarding only one time series at a time. There are several approaches which make use of the grange...

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Autores principales: Klassen, Gerhard, Tatusch, Martha, Himmelspach, Ludmila, Conrad, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274322/
http://dx.doi.org/10.1007/978-3-030-50146-4_50
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author Klassen, Gerhard
Tatusch, Martha
Himmelspach, Ludmila
Conrad, Stefan
author_facet Klassen, Gerhard
Tatusch, Martha
Himmelspach, Ludmila
Conrad, Stefan
author_sort Klassen, Gerhard
collection PubMed
description The discovery of knowledge by analyzing time series is an important field of research. In this paper we investigate multiple multivariate time series, because we assume a higher information value than regarding only one time series at a time. There are several approaches which make use of the granger causality or the cross correlation in order to analyze the influence of time series on each other. In this paper we extend the idea of mutual influence and present FCSETS (Fuzzy Clustering Stability Evaluation of Time Series), a new approach which makes use of the membership degree produced by the fuzzy c-means (FCM) algorithm. We first cluster time series per timestamp and then compare the relative assignment agreement (introduced by Eyke Hüllermeier and Maria Rifqi) of all subsequences. This leads us to a stability score for every time series which itself can be used to evaluate single time series in the data set. It is then used to rate the stability of the entire clustering. The stability score of a time series is higher the more the time series sticks to its peers over time. This not only reveals a new idea of mutual time series impact but also enables the identification of an optimal amount of clusters per timestamp. We applied our model on different data, such as financial, country related economy and generated data, and present the results.
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spelling pubmed-72743222020-06-05 Fuzzy Clustering Stability Evaluation of Time Series Klassen, Gerhard Tatusch, Martha Himmelspach, Ludmila Conrad, Stefan Information Processing and Management of Uncertainty in Knowledge-Based Systems Article The discovery of knowledge by analyzing time series is an important field of research. In this paper we investigate multiple multivariate time series, because we assume a higher information value than regarding only one time series at a time. There are several approaches which make use of the granger causality or the cross correlation in order to analyze the influence of time series on each other. In this paper we extend the idea of mutual influence and present FCSETS (Fuzzy Clustering Stability Evaluation of Time Series), a new approach which makes use of the membership degree produced by the fuzzy c-means (FCM) algorithm. We first cluster time series per timestamp and then compare the relative assignment agreement (introduced by Eyke Hüllermeier and Maria Rifqi) of all subsequences. This leads us to a stability score for every time series which itself can be used to evaluate single time series in the data set. It is then used to rate the stability of the entire clustering. The stability score of a time series is higher the more the time series sticks to its peers over time. This not only reveals a new idea of mutual time series impact but also enables the identification of an optimal amount of clusters per timestamp. We applied our model on different data, such as financial, country related economy and generated data, and present the results. 2020-05-18 /pmc/articles/PMC7274322/ http://dx.doi.org/10.1007/978-3-030-50146-4_50 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Klassen, Gerhard
Tatusch, Martha
Himmelspach, Ludmila
Conrad, Stefan
Fuzzy Clustering Stability Evaluation of Time Series
title Fuzzy Clustering Stability Evaluation of Time Series
title_full Fuzzy Clustering Stability Evaluation of Time Series
title_fullStr Fuzzy Clustering Stability Evaluation of Time Series
title_full_unstemmed Fuzzy Clustering Stability Evaluation of Time Series
title_short Fuzzy Clustering Stability Evaluation of Time Series
title_sort fuzzy clustering stability evaluation of time series
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274322/
http://dx.doi.org/10.1007/978-3-030-50146-4_50
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