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Anomaly detection for discrete sequences: A survey
Varun Chandola
, Arindam Banerjee
, Vipin Kumar
Department of Computer Science and Engineering
University at Buffalo
University of Minnesota Twin Cities
Research output
:
Contribution to journal
›
Review article
›
peer-review
539
Scopus citations
Overview
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Keyphrases
Anomaly Detection
100%
Problem Formulation
100%
Discrete Sequence
100%
Existing Techniques
33%
Application Domain
33%
Relative Strength
16%
Detection Problem
16%
Frequency of Occurrence
16%
Subsequence
16%
Online Anomaly Detection
16%
Detecting Anomalies
16%
Anomaly Detection System
16%
Long Sequence
16%
Normal Sequences
16%
Symbolic Sequences
16%
Sequence Anomaly Detection
16%
Various Applications
16%
Novelty Adaptation
16%
Time Series Anomaly Detection
16%
Computer Science
Problem Formulation
100%
Anomaly Detection
100%
Application Domain
33%
Relative Strength
16%
Detecting Anomaly
16%
Underlying Algorithm
16%
Symbolic Sequence
16%
Social Sciences
Anomaly Detection
100%
Time Series
16%