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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