Time Series Clustering and Classification

Elizabeth Ann Maharaj, Pierpaolo D'Urso, Jorge Caiado (Autoren)

Buch | Softcover

246 Seiten

2021
Chapman & Hall/CRC (Verlag)
978-1-032-09349-9 (ISBN)

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This book 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.

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

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.

1. Introduction 2. Time Series Features and Models 3. Traditional cluster analysis 4. Fuzzy clustering 5. Observation-based clustering 6. Feature-based clustering 7. Model-based clustering 8. Other time series clustering approaches 9. Feature-based classification approaches 10. Other time series classification approaches 11.Software and Data Sets

Erscheinungsdatum	01.07.2021
Reihe/Serie	Chapman & Hall/CRC Computer Science & Data Analysis
Zusatzinfo	46 Illustrations, black and white
Sprache	englisch
Maße	156 x 234 mm
Gewicht	453 g
Themenwelt	Mathematik / Informatik ► Mathematik
	Technik ► Elektrotechnik / Energietechnik
	Technik ► Umwelttechnik / Biotechnologie
	Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie
ISBN-10	1-032-09349-8 / 1032093498
ISBN-13	978-1-032-09349-9 / 9781032093499
Zustand	Neuware