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
Product details
- Hardback | 228 pages
- 156 x 235 x 20.32mm | 503g
- 12 Apr 2019
- Taylor & Francis Inc
- Productivity Press
- Portland, United States
- English
- 46 Line drawings, black and white; 42 Tables, black and white; 46 Illustrations, black and white
- 1498773214
- 9781498773218
Download Time Series Clustering and Classification (9781498773218).pdf, available at globalexpertsystems.org for free.
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