Time series clustering and classification / Elizabeth Ann Maharaj (Department of Econometrics and Business Statistics, Monash University, Australia), Pierpaolo D'Urso (Department of Social and Economic Sciences, Sapienza--University of Rome, Italy), Jorge Caiado (Department of Mathematics, ISEG, Lisbon School of Economics & Management, University of Lisbon, Portugal)

Von:

Maharaj, Elizabeth Ann [VerfasserIn]

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Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Chapman & Hall/CRC computer science and data analysis seriesVerlag: Boca Raton ; London ; New York : CRC Press, [2019]Beschreibung: 1 Online-RessourceISBN:

9780429058264
9780429603303

Schlagwörter:

Andere physische Formen: 9780429608827. | 9780429597787. | 9781498773218. | Erscheint auch als: Time series clustering and classification. Druck-Ausgabe Boca Raton : CRC Press, Taylor & Francis Group, 2019. xv, 228 SeitenLOC-Klassifikation:

QA280

Online-Ressourcen:

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Zusammenfassung: Introduction -- Time series features and models -- Traditional cluster analysis -- Fuzzy clustering -- Observation-based clustering -- Feature-based clustering -- Model-based clustering -- Other time series clustering approaches -- Feature-based approaches -- Other time series classification approaches -- Software and data sets.Zusammenfassung: The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary websitePPN: PPN: 1685649750Package identifier: Produktsigel: BSZ-4-NLEBK-KAUB | ZDB-4-NLEBK

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