Elements of information theory
Elements of information theory
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised Profiling for Identifying Superimposed Fraud
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A unifying framework for detecting outliers and change points from non-stationary time series data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Data Mining and Knowledge Discovery
Fisher information and stochastic complexity
IEEE Transactions on Information Theory
Analysis and modeling of job arrivals in a production grid
ACM SIGMETRICS Performance Evaluation Review
Detecting outlier samples in multivariate time series dataset
Knowledge-Based Systems
A Data Mining Methodology for Anomaly Detection in Network Data
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Mining closed patterns in multi-sequence time-series databases
Data & Knowledge Engineering
Cusum techniques for timeslot sequences with applications to network surveillance
Computational Statistics & Data Analysis
Anomaly Detection Using Time Index Differences of Identical Symbols with and without Training Data
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Modeling knowledge discovery in financial forecasting
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Change point detection based on call detail records
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Modeling job arrivals in a data-intensive grid
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Discovering emerging patterns for anomaly detection in network connection data
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
An incident analysis system NICTER and its analysis engines based on data mining techniques
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Detection of hidden structures in nonstationary spike trains
Neural Computation
Real-time change-point detection using sequentially discounting normalized maximum likelihood coding
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Multi-stage change-point detection scheme for large-scale simultaneous events
Computer Communications
Granular-based partial periodic pattern discovery over time series data
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Sequential change-point detection based on direct density-ratio estimation
Statistical Analysis and Data Mining
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data
Localization of mountain glacier termini in Landsat multi-spectral images
Pattern Recognition Letters
Botnet detection based on non-negative matrix factorization and the MDL principle
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Change-point detection in time-series data by relative density-ratio estimation
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Outskewer: Using Skewness to Spot Outliers in Samples and Time Series
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Neural Computation
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Data Mining and Knowledge Discovery
Mining sequential patterns with extensible knowledge representation
Intelligent Data Analysis
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We are concerned with the issue of detecting outliers and change points from time series. In the area of data mining, there have been increased interest in these issues since outlier detection is related to fraud detection, rare event discovery, etc., while change-point detection is related to event/trend change detection, activity monitoring, etc. Although, in most previous work, outlier detection and change point detection have not been related explicitly, this paper presents a unifying framework for dealing with both of them. In this framework, a probabilistic model of time series is incrementally learned using an online discounting learning algorithm, which can track a drifting data source adaptively by forgetting out-of-date statistics gradually. A score for any given data is calculated in terms of its deviation from the learned model, with a higher score indicating a high possibility of being an outlier. By taking an average of the scores over a window of a fixed length and sliding the window, we may obtain a new time series consisting of moving-averaged scores. Change point detection is then reduced to the issue of detecting outliers in that time series. We compare the performance of our framework with those of conventional methods to demonstrate its validity through simulation and experimental applications to incidents detection in network security.