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Time series clustering and classification. (English) Zbl 1435.62006

Chapman & Hall/CRC Computer Science & Data Analysis Series. Boca Raton, FL: CRC Press (ISBN 978-1-4987-7321-8/hbk; 978-0-429-05826-4/ebook). xv, 228 p. (2019).
From the cover of the book: 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.
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

From the preface of the book: “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, we find algorithms and methods for clustering and classifying different types of large datasets, including time series data, spatial data, panel data, categorical data, functional data and digital data. The emphasis of this book is on the clustering and classification of time series data, and it can be regarded as a reference manual on this topic. …A comprehensive webpage providing additional material to support this book can be found at http://www.tsclustering.homepage.pt/.”
The book is very large structured in a preface, 11 chapters (divided in 65 subchapters), bibliography, subject index:
Chapter 1. Introduction – Chapter 2. Time Series Features and Models
Part I. “Unsupervised Approaches: Clustering Techniques for Time Series” with the Chapter 3. Traditional cluster analysis – Chapter 4. Fuzzy clustering – Chapter 5. Observation-based clustering – Chapter 6. Feature-based clustering – Chapter 7. Model-based clustering – Chapter 8. Other time series clustering approaches
Part II. “Supervised Approaches: Classification Techniques for Time Series” with the Chapter 9. Feature-based classification approaches – Chapter 10. Other time series classification approaches
Part III. “Software and Data Sets” with the Chapter 11. Software and Data Sets (with useful links to relevant websites)
All Chapters 2. to 11. start with an introduction. The bibliography contains 251 references and the Index more than 190 items.
The book can be recommend all readers, who are interested in this field.

MSC:

62-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62H30 Classification and discrimination; cluster analysis (statistical aspects)
91C20 Clustering in the social and behavioral sciences
62M05 Markov processes: estimation; hidden Markov models

Software:

R; Matlab
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