Abstract
The prediction of long range dependence (LRD) is the critical problem in network traffic. The traditional algorithms, such as Markov model and ON/OFF model, may provide high computation cost and low precision. In this study, a novel method based on empirical mode decomposition (EMD) and ARMA model was proposed. The results show that EMD could offer the function of canceling the LRD in traffic data. After transforming LRD to SRD (short range dependence) by EMD processing, the LRD traffic data could be predicted with high accuracy and low complexity by ARMA model. Meanwhile, the results indicate the usefulness of EMD in the applications of network traffic prediction.
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Gao, B., Zhang, Q., Zhang, N. (2012). LRD Traffic Predicting Based on ARMA. In: Ren, P., Zhang, C., Liu, X., Liu, P., Ci, S. (eds) Wireless Internet. WICON 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30493-4_45
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DOI: https://doi.org/10.1007/978-3-642-30493-4_45
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