![]() We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. Compared to general outlier detection, techniques for temporal outlier detection are very different. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. A large number of applications generate temporal datasets. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. Initial research in outlier detection focused on time series-based outliers (in statistics). U.S.Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Indiana University Purdue University of Indianapolis King County GIS Center, Seattle, WashingtonĬentro Internacional de Agricultura Tropical Singapore University of Technology & Design ![]() University at Buffalo, State University of New York Hunter College, City University of New York John Wilson, Editor-in-Chief of the GIS&T Body of Knowledge, and University of Southern California That said, we would like to recognize and express our gratitude to the following individuals for refereeing manuscripts submitted to the University Consortium for Geographic Information Science’s Geographic Information Science & Technology Body of Knowledge project since this digital version was launched in summer 2016.ĭiana Sinton, GIS&T Body of Knowledge Project Manager, and Executive Director, UCGIS ![]() The success of every academic project is due in part to the contributions of the referees who help the editors to identify and guide the best manuscripts to publication as quickly as possible. ![]()
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