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

chapter 1|7 pages


chapter 2|17 pages

Time series features and models

part Part I|1 pages

Unsupervised Approaches: Clustering Techniques for Time Series

chapter 3|10 pages

Traditional cluster analysis

chapter 4|11 pages

Fuzzy clustering

chapter 5|18 pages

Observation-based clustering

chapter 6|43 pages

Feature-based clustering

chapter 7|41 pages

Model-based clustering

chapter 8|8 pages

Other time series clustering approaches

part Part II|1 pages

Supervised Approaches: Classification Techniques for Time Series

chapter 9|25 pages

Feature-based approaches

part Part III|1 pages

Software and Data Sets

chapter 11|6 pages

Software and data sets