Time Series Clustering and Classification (Hardcover)

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

General

Imprint:	Productivity Press
Country of origin:	United States
Series:	Chapman & Hall/CRC Computer Science & Data Analysis
Release date:	April 2019
First published:	2019
Authors:	Elizabeth Ann Maharaj • Pierpaolo D'Urso • Jorge Caiado
Dimensions:	234 x 156 x 14mm (L x W x T)
Format:	Hardcover
Pages:	228
ISBN-13:	978-1-4987-7321-8
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Professional & Technical > Environmental engineering & technology > General Books > Business & Economics > Economics > Econometrics > Economic statistics Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	1-4987-7321-4
Barcode:	9781498773218

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