Novel anomaly detection approach for telecommunication network proactive performance monitoring

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.

Keywords

proactive performance monitoring (PPM) / anomaly detection / time series prediction / autoregressive integrated moving average (ARIMA) / white noise / confidence interval

References

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Acknowledgements

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

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg