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Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (pages 91-108).

1. Introduction and challenges -- 1.1 Temporal outlier examples -- 1.2 Different facets of temporal outlier analysis -- 1.3 Specific challenges for outlier detection for temporal data -- 1.4 Conclusions and summary --

2. Outlier detection for time series and data sequences -- 2.1 Outliers in time series databases -- 2.1.1 Direct detection of outlier time series -- 2.1.2 Window-based detection of outlier time series -- 2.1.3 Outlier subsequences in a test time series -- 2.1.4 Outlier points across multiple time series -- 2.2 Outliers within a given time series -- 2.2.1 Points as outliers -- 2.2.2 Subsequences as outliers -- 2.3 Conclusions and summary --

3. Outlier detection for data streams -- 3.1 Evolving prediction models -- 3.1.1 Online sequential discounting -- 3.1.2 Dynamic cluster maintenance -- 3.1.3 Dynamic Bayesian networks (DBNS) -- 3.2 Distance-based outliers for sliding windows -- 3.2.1 Distance-based global outliers -- 3.2.2 Distance-based local outliers -- 3.3 Outliers in high-dimensional data streams -- 3.4 Detecting aggregate windows of change -- 3.5 Supervised methods for streaming outlier detection -- 3.6 Conclusions and summary --

4. Outlier detection for distributed data streams -- 4.1 Examples and challenges -- 4.2 Sharing data points -- 4.3 Sharing local outliers and other data points -- 4.4 Sharing model parameters -- 4.5 Sharing local outliers and data distributions -- 4.6 Vertically partitioned distributed data -- 4.7 Conclusions and summary --

5. Outlier detection for spatio-temporal data -- 5.1 Spatio-temporal outliers (ST-outliers) -- 5.1.1 Density-based outlier detection -- 5.1.2 Outlier detection using spatial scaling -- 5.1.3 Outlier detection using Voronoi diagrams -- 5.2 Spatio-temporal outlier solids -- 5.2.1 Using Kulldorff scan statistic -- 5.2.2 Using image processing -- 5.3 Trajectory outliers -- 5.3.1 Distance between trajectories -- 5.3.2 Direction and density of trajectories -- 5.3.3 Historical similarity -- 5.3.4 Trajectory motifs -- 5.4 Conclusions and summary --

6. Outlier detection for temporal network data -- 6.1 Outlier graphs from graph time series -- 6.1.1 Weight independent metrics -- 6.1.2 Metrics using edge weights -- 6.1.3 Metrics using vertex weights -- 6.1.4 Scan statistics -- 6.2 Multi-level outlier detection from graph snapshots -- 6.2.1 Elbows, broken correlations, prolonged spikes, and lightweight stars -- 6.2.2 Outlier node pairs -- 6.3 Community-based outlier detection algorithms -- 6.3.1 Community outliers using community change patterns -- 6.3.2 Change detection using minimum description length -- 6.3.3 Community outliers using evolutionary clustering -- 6.4 Online graph outlier detection algorithms -- 6.4.1 Spectral methods -- 6.4.2 Structural outlier detection -- 6.5 Conclusions and summary --

7. Applications of outlier detection for temporal data -- 7.1 Temporal outliers in environmental sensor data -- 7.2 Temporal outliers in industrial sensor data -- 7.3 Temporal outliers in surveillance and trajectory data -- 7.4 Temporal outliers in computer networks data -- 7.5 Temporal outliers in biological data -- 7.6 Temporal outliers in astronomy data -- 7.7 Temporal outliers in web data -- 7.8 Temporal outliers in information network data -- 7.9 Temporal outliers in economics time series data -- 7.10 Conclusions and summary --

8. Conclusions and research directions -- Bibliography -- Authors' biographies.

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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. Initial research in outlier detection focused on time series-based outliers (in statistics). 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. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book.

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Title from PDF title page (viewed on April 22, 2014).