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
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
Time-series classification is the common theoretical background of many recognition tasks performed by computers, such as handwriting recognition, speech recognition or detection of abnormalities in...
In many areas of applied statistics like economics or finance it is often desirable to find groups of similar time series in a set or panel of time series. Therefore, clustering techniques are...
Patterns can be any number of items that occur repeatedly, whether in the behaviour of animals, humans, traffic, or even in the appearance of a design. As technologies continue to advance,...
Determining risk-adequate insurance premiums is a core issue in actuarial mathematics. This study is specifically concerned with identifying convenient partitions of (general) insurance collectives...
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