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Author Baikovicius, J. ♦ Gerencser, L.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1990
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Other branches of engineering
Subject Keyword Stochastic processes ♦ Power system dynamics ♦ Predictive models ♦ Power system modeling ♦ Polynomials ♦ Encoding ♦ Prediction algorithms ♦ Stochastic systems ♦ Linear systems ♦ Mathematical model
Abstract The authors present a method, inspired by stochastic complexity theory, for solving the change point detection problem for ARMA (autoregressive moving average) systems which are assumed to have a slow unstructured nondecaying drift after the change has occurred. The central idea is to apply the minimum description length method in the form of predictive stochastic complexity, which gives a way for selecting the best model among a given set of models. Therefore the change point detection problem is reduced to a model selection problem. Simulations that show that the approach exhibits good detection capabilities are included.<<ETX>>
Description Author affiliation: Dept. of Electr. Eng., McGill Univ., Montreal, Que., Canada (Baikovicius, J.; Gerencser, L.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1990-12-05
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 198.33 kB
Page Count 2
Starting Page 3554
Ending Page 3555


Source: IEEE Xplore Digital Library