Traffic Forecast Based on Empirical Mode Decomposition and RBF Neural Network

Shi Bao Jiang; Chuan Feng Bai; Cui Feng Du

doi:10.4028/www.scientific.net/AMR.846-847.1270

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 846-847Traffic Forecast Based on Empirical Mode...

Traffic Forecast Based on Empirical Mode Decomposition and RBF Neural Network

Abstract:

Traffic analysis and forecast are the classic topics of telecommunication research, as they provide strategic ground to address the issues such as mobile networks traffic jam, network coverage planning design, marketing management etc. On the basis of the empirical mode decomposition theory and methods, this article first implements multi-scale analysis of the time series of traffic, then it goes on to executes RBF neural network based on the different compositions, and finally reach the forecast expectation of voice traffics of selected base stations.

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Periodical:

Advanced Materials Research (Volumes 846-847)

Pages:

1270-1273

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.846-847.1270

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Online since:

November 2013

Authors:

Shi Bao Jiang*, Chuan Feng Bai, Cui Feng Du

Keywords:

Empirical Mode Decomposition (EMD), Regression Analysis, Time Series Forecast, Traffic

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* - Corresponding Author

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