1st Edition

Time Series Clustering and Classification

ISBN 9781498773218
Published April 12, 2019 by Chapman and Hall/CRC
228 Pages 46 B/W Illustrations

USD $174.95

Prices & shipping based on shipping country


Book Description

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


Time Series Features and Models

Traditional cluster analysis

Fuzzy clustering

Observation-based clustering

Feature-based clustering

Model-based clustering

Other time series clustering approaches

Feature-based classification approaches

Other time series classification approaches

Software and Data Sets

View More



Elizabeth Ann Maharaj is an Associate Professor in the Department of Econometrics and Business Statistics at Monash University, Australia. She has a Ph.D. from Monash University on the Pattern Recognition of Time Series. Ann is an elected member of the International Statistical Institute (ISI), a member of the International Association of Statistical Computing (IASC) and of the Statistical Society of Australia (SSA). She is also an accredited statistician with the SSA. Ann’s main research interests are in time series classification, wavelets analysis, fuzzy classification and interval time series analysis. She has also worked on research projects in climatology, environmental science, labour markets, human mobility and finance.

Pierpaolo D'Urso is a Full Professor of Statistics at Sapienza - University of Rome. He is the chair of the Department of Social and Economic Sciences, Sapienza - University of Rome. He received his Ph.D. in Statistics and his bachelor's degree in Statistics both from Sapienza. He is an associate editor and a member of the editorial board of several journals. He has been member of several program committees of international conferences and guest editor of special issues. His recent research activity is focus on fuzzy clustering, clustering and classification of time series, clustering of complex structures of data, and statistical methods for marketing, local labour systems, electoral studies and environmental monitoring.

Jorge Caiado has a Ph.D. in Applied Mathematics to Economics and Management. He is a Professor of Econometrics and Forecasting Methods at the Lisbon School of Economics and Management (ISEG) and a Researcher at the Centre for Applied Mathematics and Economics. His research in econometrics, finance, time series analysis, forecasting methods and statistical software has led to numerous publications in scientific journals and books. He serves as an econometric and statistical consultant and trainer for numerous companies and organizations including central banks, commercial and investment banks, bureau of statistics, bureau of economic analysis, transportation and logistics companies, health companies and insurance companies. He is also a co-founder and partner of GlobalSolver.


"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