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Guaranteed Network Traffic Demand Prediction Using FARIMA Models

  • Mikhail Dashevskiy
  • Zhiyuan Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

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.

Keywords

Expert Advice Hurst Parameter Good Expert Time Series Process Demand Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mikhail Dashevskiy
    • 1
  • Zhiyuan Luo
    • 1
  1. 1.Computer Learning Research Centre,Royal HollowayUniversity of LondonEghamUK

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