Guaranteed Network Traffic Demand Prediction Using FARIMA Models

Authors:
Mikhail Dashevskiy;Zhiyuan Luo
Affiliations:
Computer Learning Research Centre,Royal Holloway, University of London, Egham, UK TW20 0EX;Computer Learning Research Centre,Royal Holloway, University of London, Egham, UK TW20 0EX
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

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Abstract

The Fractional Auto-Regressive Integrated Moving Average (FARIMA) model is often used to model and predict network traffic demand which exhibits both long-range and short-range dependence. However, finding the best model to fit a given set of observations and achieving good performance is still an open problem. We present a strategy, namely Aggregating Algorithm, which uses several FARIMA models and then aggregates their outputs to achieve a guaranteed (in a sense) performance. Our feasibility study experiments on the public datasets demonstrate that using the Aggregating Algorithm with FARIMA models is a useful tool in predicting network traffic demand.