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.
- 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
part Part I|1 pages
Unsupervised Approaches: Clustering Techniques for Time Series
part Part II|1 pages
Supervised Approaches: Classification Techniques for Time Series
part Part III|1 pages
Software and Data Sets