Section
Completed
Onsite
Lecture
Anomaly detection methods and applications
About This Event
Speaker: Dalia Kriksciuniene, Assoc.prof.dr. (ERCIM researcher at
Masaryk University, Brno, Czech)
Title: Anomaly detection methods and applications
Abstract:
Numerous statistical, econometrical and intelligent
methods are researched in the scientific works and industrial applications for
finding repeating patterns and approximated distribution rules of data series.
The main problem experienced by the scientific research is lack of regularities
and rather chaotic nature of real data. That hinders performance of the
forecasting methods based on finding regularities in data distribution, limits
their application to analytics of historical data repositories, and reveals
high unreliability of the models to react to occurring external impacts for
real-time data streams.
The current
research aims to explore application of computational analysis methods for
detecting behaviour anomalies. The methods and the principles of anomaly
detection are analysed from the theoretical perspective and evaluated for their
ability to serve for analysis, forecasting and designing optimal strategies for
the enterprises operation in various application domains. They include analysis of anomalies of the
financial markets, caused by changes of investor activeness due to different
calendar effects, impacts of various media announcements and news, fluctuations
of market situations, including crises and bubbles. The anomaly detection is
important in the areas of facility management based on sensor data system,
detection of network intrusions and performance failures.
The results of currently published research cover several
methods for detecting behaviour anomalies including information efficiency
evaluation methods (Shannon's entropy, Hurst exponent), event impacts explored
by the methods of interrupted time series, and evaluation of binary clustering
algorithms.
The research results are evaluated by discussing the potential
power of explored methods for detection behaviour anomalies in the expanding
research area of Big Data.
Masaryk University, Brno, Czech)
Title: Anomaly detection methods and applications
Abstract:
Numerous statistical, econometrical and intelligent
methods are researched in the scientific works and industrial applications for
finding repeating patterns and approximated distribution rules of data series.
The main problem experienced by the scientific research is lack of regularities
and rather chaotic nature of real data. That hinders performance of the
forecasting methods based on finding regularities in data distribution, limits
their application to analytics of historical data repositories, and reveals
high unreliability of the models to react to occurring external impacts for
real-time data streams.
The current
research aims to explore application of computational analysis methods for
detecting behaviour anomalies. The methods and the principles of anomaly
detection are analysed from the theoretical perspective and evaluated for their
ability to serve for analysis, forecasting and designing optimal strategies for
the enterprises operation in various application domains. They include analysis of anomalies of the
financial markets, caused by changes of investor activeness due to different
calendar effects, impacts of various media announcements and news, fluctuations
of market situations, including crises and bubbles. The anomaly detection is
important in the areas of facility management based on sensor data system,
detection of network intrusions and performance failures.
The results of currently published research cover several
methods for detecting behaviour anomalies including information efficiency
evaluation methods (Shannon's entropy, Hurst exponent), event impacts explored
by the methods of interrupted time series, and evaluation of binary clustering
algorithms.
The research results are evaluated by discussing the potential
power of explored methods for detection behaviour anomalies in the expanding
research area of Big Data.
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