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Published in:
Redefining Information Processing Through Neural Computing Models Read chapter Read first chapter

2018 | OriginalPaper | Chapter

14. An Application of Internet Traffic Prediction with Deep Neural Network

Authors : Sanam Narejo, Eros Pasero

Published in: Multidisciplinary Approaches to Neural Computing

Publisher: Springer International Publishing

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Abstract

The advance knowledge of future traffic load is helpful for network service providers to optimize the network resource and to recover the demand criteria. This paper presents the task of internet traffic prediction with three different architectures of Deep Belief Network (DBN). The artificial neural network is created with the depth of 4 hidden layers in each model to learn the nonlinear hierarchal essence present in the time series of internet traffic data. The deep learning in the network is executed with unsupervised pretraining of the layers. The emphasis is given to the topology of DBN that achieves excellent prediction accuracy. The adopted approach provides accurate traffic predictions while simulating the traffic data patterns and stochastic elements, achieving 0.028 Root Mean Square Error (RMSE) value on the test data set. To validate our choice for hidden layer size selection, further more experiments were done for chaotic time series prediction.

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previous chapter Rule Base Reduction Using Conflicting and Reinforcement Measures
next chapter Growing Curvilinear Component Analysis (GCCA) for Dimensionality Reduction of Nonstationary Data
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Metadata
Title
An Application of Internet Traffic Prediction with Deep Neural Network
Authors
Sanam Narejo
Eros Pasero
Copyright Year
2018
Book
Multidisciplinary Approaches to Neural Computing

Print ISBN: 978-3-319-56903-1

Electronic ISBN: 978-3-319-56904-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-56904-8_14

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