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Time series clustering and classification

Author: Elizabeth Ann Maharaj; Pierpaolo D'Urso; Jorge Caiado
Publisher: Boca Raton, Florida : CRC Press, [2019]
Series: Series in computer science and data analysis.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Maharaj, Elizabeth Ann.
Time series clustering and classification.
Boca Raton, Florida : CRC Press, [2019]
(DLC) 2018052974
(OCoLC)1079410240
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Elizabeth Ann Maharaj; Pierpaolo D'Urso; Jorge Caiado
ISBN: 9780429058264 0429058268 9780429603303 0429603304 9780429608827 0429608829 9780429597787 0429597789
OCLC Number: 1090301457
Description: 1 online resource.
Contents: 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.
Series Title: Series in computer science and data analysis.
Responsibility: 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).

Abstract:

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 website

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