Time Series Clustering and Classification

✍ Scribed by Elizabeth Ann Maharaj (Author); Pierpaolo D`Urso (Author); Jorge Caiado (Author)

• Publisher: Chapman and Hall/CRC
• Year: 2019
• Leaves: 245
• 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.

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

✦ Table of Contents

Introduction

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