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» Assumption-Free Anomaly Detection in Time Series
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SIGPRO
2008
136views more  SIGPRO 2008»
13 years 5 months ago
Estimation of slowly varying parameters in nonlinear systems via symbolic dynamic filtering
This paper introduces a novel method for real-time estimation of slowly varying parameters in nonlinear dynamical systems. The core concept is built upon the principles of symboli...
Venkatesh Rajagopalan, Subhadeep Chakraborty, Asok...
GECCO
2006
Springer
178views Optimization» more  GECCO 2006»
13 years 9 months ago
A dynamic approach to artificial immune systems utilizing neural networks
The purpose of this work is to propose an immune-inspired setup to use a self-organizing map as a computational model for the interaction of antigens and antibodies. The proposed ...
Stefan Schadwinkel, Werner Dilger
DATAMINE
2008
219views more  DATAMINE 2008»
13 years 5 months ago
Correlating burst events on streaming stock market data
Abstract We address the problem of monitoring and identification of correlated burst patterns in multi-stream time series databases. We follow a two-step methodology: first we iden...
Michail Vlachos, Kun-Lung Wu, Shyh-Kwei Chen, Phil...
NIPS
2007
13 years 6 months ago
Measuring Neural Synchrony by Message Passing
A novel approach to measure the interdependence of two time series is proposed, referred to as “stochastic event synchrony” (SES); it quantifies the alignment of two point pr...
Justin Dauwels, François B. Vialatte, Tomas...
INFOCOM
2009
IEEE
14 years 2 days ago
Measuring Complexity and Predictability in Networks with Multiscale Entropy Analysis
—We propose to use multiscale entropy analysis in characterisation of network traffic and spectrum usage. We show that with such analysis one can quantify complexity and predict...
Janne Riihijärvi, Matthias Wellens, Petri M&a...