Time Series Clustering and Classification

Abstract

As we enter the big data era, the amount of time-series data is growing especially fast, originating from various sources like web traffic, healthcare sensors, etc. To understand how these time series connect or to identify anomalies in datasets, a fundamental question is: How do we cluster or classify these time series? Good clustering or classification results should provide useful insights from the dataset and help people make critical decisions. The authors of this book have more than 20 years of experience on the topic of time series clustering and classification. They consolidate many important methods and algorithms commonly used in time series clustering and classification practices published by various scientific journals. In addition, they provide Matlab and R code and corresponding datasets to reproduce the examples in the book.