Time Series Clustering and Classification

The age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists, and others. Time Series Clustering and Classification provides an overview of techniques for clustering and classifying different types of large datasets, including time series data. It covers unsupervised and supervised approaches and includes real examples from medicine, finance, environmental science, and more. R and MATLAB code and relevant data sets are available on a supplementary website.

Format: Paperback / softback
Length: 246 pages
Publication date: 30 June 2021
Publisher: Taylor & Francis Ltd

The advent of artificial intelligence and machine learning has brought forth a transformative era, presenting both intriguing challenges and abundant opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists, and a multitude of other professionals. At the heart of these advanced techniques lie algorithms and methodologies for efficiently clustering and classifying diverse types of large datasets, including time series data.

Time Series Clustering and Classification delve into the realm of observation-based, feature-based, and model-based traditional and fuzzy clustering methods, feature-based and model-based classification techniques, and machine learning methods. This comprehensive resource offers a thorough and self-contained exploration of techniques, catering to both researchers and students alike.

Key Features:

Comprehensive Coverage: This book provides a comprehensive overview of the methods and applications of time series pattern recognition. It encompasses a wide range of techniques, including unsupervised and supervised approaches, ensuring a comprehensive understanding of the field.

Wide Range of Techniques: The book covers a diverse array of techniques, encompassing unsupervised and supervised approaches, allowing readers to explore a broad spectrum of methods for time series analysis.

Real-World Examples: Real-world examples from various domains, such as medicine, finance, environmental science, and more, are included to illustrate the practical applications of the discussed techniques. This enhances the understanding and relevance of the content.

R and MATLAB Code: Extensive R and MATLAB code is provided, making it easier for readers to implement and experiment with the discussed methods. Additionally, relevant data sets are available on a supplementary website, further enhancing the learning experience.

Whether you are a seasoned data analyst, a researcher seeking to expand your knowledge, or a student embarking on a journey into time series analysis, Time Series Clustering and Classification is an invaluable resource. It provides a solid foundation for understanding and applying these advanced techniques, enabling you to make informed decisions based on data and extract valuable insights.

Weight: 454g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032093499