Time Series Clustering and Classification

✍ Scribed by Elizabeth Ann Maharaj, Pierpaolo D'Urso, Jorge Caiado

• Publisher: Chapman and Hall/CRC
• Year: 2019
• Tongue: English
• Leaves: 245
• Series: Chapman & Hall/CRC Computer Science & Data Analysis
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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