Time Series Clustering and Classification

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. 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 Dedications Contents Preface Authors 1 Introduction 2 Time series features and models Part I: Unsupervised Approaches: Clustering Techniques for Time Series 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 Part II: Supervised Approaches: Classification Techniques for Time Series 9 Feature-based approaches 10 Other time series classification approaches Part III: Software and Data Sets 11 Software and data sets Bibliography Subject index