Detecting changes in the scale of dependent Gaussian processes: a large deviations approach

Kuhn, Julia, Ellens, Wendy and Mandjes, Michel (2014). Detecting changes in the scale of dependent Gaussian processes: a large deviations approach. In: Analytical and Stochastic Modelling Techniques and Applications: 21st International Conference: ASMTA 2014: Proceedings. 21st International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2014, Budapest, Hungary, (170-184). 30 June - 2 July 2014. doi:10.1007/978-3-319-08219-6_12


Author Kuhn, Julia
Ellens, Wendy
Mandjes, Michel
Title of paper Detecting changes in the scale of dependent Gaussian processes: a large deviations approach
Conference name 21st International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2014
Conference location Budapest, Hungary
Conference dates 30 June - 2 July 2014
Proceedings title Analytical and Stochastic Modelling Techniques and Applications: 21st International Conference: ASMTA 2014: Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2014
Sub-type Fully published paper
DOI 10.1007/978-3-319-08219-6_12
Open Access Status
ISBN 9783319082189
9783319082196
ISSN 1611-3349
0302-9743
Volume 8499
Start page 170
End page 184
Total pages 15
Collection year 2015
Language eng
Abstract/Summary This paper devises new hypothesis tests for detecting changes in the scale of interdependent and serially correlated data streams, i.e, proportional changes of the mean and (co-)variance. Such procedures are of great importance in various networking contexts, since they enable automatic detection of changes, e.g. in the network load. Assuming the underlying structure is Gaussian, we compute the log-likelihood ratio test statistic, either as a function of the observations themselves or as a function of the innovations (i.e., a sequence of i.i.d. Gaussians, to be extracted from the observations). An alarm is raised if the test statistic exceeds a certain threshold. Based on large deviations techniques, we demonstrate how the threshold is chosen such that the ratio of false alarms is kept at a predefined (low) level. Numerical experiments validate the procedure, and demonstrate the merits of a multidimensional detection approach (over multiple one-dimensional tests). Also a detailed comparison between the observations-based approach and the innovations-based approach is provided.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Collections: School of Mathematics and Physics
Official 2015 Collection
 
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