Outlier detection for temporal data

Content: Introduction and challenges --
Outlier detection for time series and data sequences --
Outlier detection for data streams --
Outlier detection for distributed data streams --
Outlier detection for spatio-temporal data --
Outlier detection for temporal network data --Applications of outlier detection for temporal data --
Conclusions and research directions. 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.