Anzeige
Erschienen in:
2008 | OriginalPaper | Buchkapitel
Guaranteed Network Traffic Demand Prediction Using FARIMA Models
verfasst von : Mikhail Dashevskiy, Zhiyuan Luo
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2008
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.