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Time Series Clustering and Classification
Chapman & Hall/CRC Computer Science & Data Analysis
Genres:
Economic statistics, Probability & statistics, Automatic control engineering, Environmental science, engineering & technology
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Synopsis
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 New & Used
| Seller | Information | Condition | Price | |
|---|---|---|---|---|
 | - | New | £127.23 + FREE UK P & P |
What Reviewers Are Saying
"The book represents 20 years of research by the authors. They have achieved the goal of gathering in one place a broad spectrum of clustering and classification techniques for time series, which have attracted substantial attention for the last few decades...The book contains a number of examples of clustering, which are intended to highlight the main theoretical models on real data...The book contains a large amount of theoretical information and practical examples and may be recommended as a desk book for young scientists and applied mathematicians."
- Maria Ivanchuk, ISCB News, July 2020
"The authors of this book have more than 20 years of experience on the topic of time series clustering and classification. They consolidate many important methods and algorithms commonly used in time series clustering and classification practices published by various scientific journals. In addition, they provide Matlab and R code and corresponding datasets to reproduce the examples in the book...This book covers most classical and common techniques for time series clustering and classification. It consolidates different methods into an extensive coherent framework. This makes the book a good reference for students and researchers."
- Ming Chen, JASA, August 2020
- Maria Ivanchuk, ISCB News, July 2020
"The authors of this book have more than 20 years of experience on the topic of time series clustering and classificati
- Ming Chen, JASA, August 2020