Novel hybrid approach to data-packet-flow prediction for improving network traffic analysis

Forecast of the flow of data packets between client and server for a network traffic analysis is viewed as a part of web analytics. Thousands of web-smart businesses depend on web analytics to improve website conversions, reduce marketing costs, facilitate website optimization, speed-up website monitoring and provide a higher level of service to their customers and partners. This paper particularly intends to develop a high accurate prediction as one of core component of network traffic analysis. In this study, a novel hybrid approach, combining adaptive neuro-fuzzy inference system (ANFIS) with nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH), is tuned optimally by quantum minimization (QM) and then applied to forecasting the flow of data packets around website. The composite model (QM-ANFIS/NGARCH) is setup in the forecast point of view to improve the predictive accuracy because it can resolve the problems of the overshoot and volatility clustering simultaneously within time series. As part of real-time intelligence web analytics, the high accurate prediction will aid webmaster to improve the throughput of data-packet-flow up to around 20%, with helping each webmaster to optimize their website, maximize online marketing conversions, and lead campaign tracking.