A practical approach to forecast Quality of Service parameters considering outliers

Autoregressive integrated moving average (ARIMA) models are used in different researches for modelling and forecasting of traffic and Quality of Service (QoS) parameter values in telecommunication networks to make reasonable short, mediumand long-term predictions. We propose methodology to use ARIMA models for QoS prediction in network scenarios based on a preliminary detection and elimination of outliers in the time series data. Outliers are patterns describing QoS behaviour caused by fault events (route failure, operation anomalies, DoS attacks, misconfiguration, etc). Practically, we show the feasibility of combination of ARIMA prediction with outlier detection for short and medium term forecasting (daily and monthly) using real world end-to-end delay QoS measurement data.