Abstract:

Fault correction by detecting anomalies designating performance degradation is an important approach to improving the reliability of communication network. Statistical hypotheses testing approach is employed to detect network anomaly. A new approach to acquiring the fluctuation threshold is proposed comprehensively when taking advantage of time series prediction confidence interval computation based on multiplicative autoregressive integrated moving average. Furthermore, under the assumption that the training residual which is a white noise process follows normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree by constructing random variable satisfying t distribution. Experiments verify the effectiveness of anomaly detection mechanism and the accuracy of the algorithm. 

Key words: anomaly detection, time series prediction, seasonal autorgressive integrated moving average, confidence interval