Outlier detection for temporal data /

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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...

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Bibliographic Details
Main Authors: Gupta, Manish (Author), Aggarwal, Charu C. (Author), Gao, Jing (Author), Han, Jiawei (Author)
Format: eBook
Language:English
Published: San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2014.
Series:Synthesis digital library of engineering and computer science.
Synthesis lectures on data mining and knowledge discovery ; # 8.
Subjects:
Outliers (Statistics)
Temporal databases.
data streams
distributed data streams
spatiotemporal outliers
temporal networks
temporal outlier detection
time series data
Online Access:Abstract with links to full text
Table of Contents:
  • 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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