Time series clustering and classification
Author(s)
Bibliographic Information
Time series clustering and classification
（Series in computer science and data analysis）
CRC Press, 2021
- : pbk
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Note
Includes bibliographical references (p. 205-223) and index
"First issued in paperback 2021"--T.p. verso
Description and Table of Contents
Description
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
Table of Contents
1. Introduction 2. Time Series Features and Models 3. Traditional cluster analysis 4. Fuzzy clustering 5. Observation-based clustering 6. Feature-based clustering 7. Model-based clustering 8. Other time series clustering approaches 9. Feature-based classification approaches 10. Other time series classification approaches 11.Software and Data Sets
by "Nielsen BookData"
