RESEARCH ARTICLE

Novel anomaly detection approach for telecommunication network proactive performance monitoring

  • Yanhua YU ,
  • Jun WANG ,
  • Xiaosu ZHAN ,
  • Junde SONG
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  • School of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received date: 16 Oct 2008

Accepted date: 10 Nov 2008

Published date: 05 Sep 2009

Copyright

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

Abstract

The mode of telecommunications network management is changing from “network oriented” to “subscriber oriented”. Aimed at enhancing subscribers’ feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In this paper, a novel anomaly detection approach is the proposed time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average (ARIMA). Furthermore, under the assumption that the training residual is a white noise process following a normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree 1-α by constructing random variables satisfying t distribution. Experimental results verify the method’s effectiveness.

Cite this article

Yanhua YU , Jun WANG , Xiaosu ZHAN , Junde SONG . Novel anomaly detection approach for telecommunication network proactive performance monitoring[J]. Frontiers of Electrical and Electronic Engineering, 2009 , 4(3) : 307 -312 . DOI: 10.1007/s11460-009-0051-9

Acknowledgements

This work was supported by the National Key Technologies R&D program of China during the 11th Five-Year Plan Period (No. 2006BAH02A03).
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