A Traffic Prediction Method Based on the Hybrid Model of CLWT

Abstract:

Current prediction models for network traffic cannot accurately depict the multi-properties of the Internet traffic. This paper proposes a wavelet-based hybrid model prediction method for network traffic called CLWT model and proposes a prediction method for traffic based on this model. The traffic time series can be rapidly decomposed respectively into approximate time series and detail time series with LF and HF response. The approximate time series predicts by making use of Least Squares Support Vector Machine and proceeds error calibration by using Generalized Recurrent Nerve Network. The detail time series predict it by making use of self-adaption chaotic prediction methods after the medium-soft threshold noise reduction. Finally the prediction value of time series is got by making use of promoting wavelet reconstitution. The effectiveness for the prediction methods mentioned in the paper has been validated by simulation experiment. High prediction accuracy is obtained compared with the existing methods.

• Periodical: Applied Mechanics and Materials (Volumes 713-715)
• Pages: 1564-1569
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.713-715.1564
• Online since: January 2015
• Authors: Jin Long Fei*, Wei Lin, Tao Han, Yue Fei Zhu
• Keywords: Chaotic Prediction, CLWT Model, Time Series, Traffic Prediction

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