Time Series Clustering and Classification Hardback
by Elizabeth Ann Maharaj, Pierpaolo D'Urso, Jorge (Centre for Applied Mathematics and Economics and ISEG, University of Lisbon, Portugal Caiado
Part of the Chapman & Hall/CRC Computer Science & Data Analysis series
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. FeaturesProvides an overview of the methods and applications of pattern recognition of time seriesCovers a wide range of techniques, including unsupervised and supervised approachesIncludes a range of real examples from medicine, finance, environmental science, and moreR and MATLAB code, and relevant data sets are available on a supplementary website
- Format: Hardback
- Pages: 228 pages, 46 Line drawings, black and white; 42 Tables, black and white; 46 Illustrations, black an
- Publisher: Taylor & Francis Inc
- Publication Date: 18/03/2019
- Category: Economic statistics
- ISBN: 9781498773218
- EPUB from £40.49
- PDF from £40.49