Forecasting the flow of data packets for website traffic analysis – ASVR-Tuned ANFIS/NGARCH approach

Authors:
Bao Rong Chang;Shi-Huang Chen;Hsiu Fen Tsai
Affiliations:
Department of Computer Science and Information Engineering, National Taitung University, Taiwan;Department of Computer Science and Information Engineering, Shu-Te University, Kaohsiung, Taiwan;Department of International Business, Shu-Te University, Kaohsiung, Taiwan
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

Forecast of the flow of data packets between client and server for a website 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, website optimization, website monitoring and provide a higher level of service to their customers and partners. This paper particularly intends to develop a high-accuracy prediction approach as the need for a website traffic analysis. The proposed composite model (ASVR-ANFIS/NGARCH) is schemed to build a systematic structure such that it is not only to improve the predictive accuracy because of resolving the problems of the overshoot and volatility clustering simultaneously, but also to boost website tracking capacity helping each webmaster to optimize their website, maximize online marketing conversions and lead campaign tracking.