Result: Web traffic prediction with artificial neural networks

Title:
Web traffic prediction with artificial neural networks
Authors:
GLUSZEK, Adam, KEKEZ, Michal, RUDZINSKI, Filip
Source:
Photonics applications in astronomy, communications, industry, and high-energy physics experiments III (Wilga, 26-30 May 2004)SPIE proceedings series. :520-525
Publisher Information:
Bellingham WA: SPIE, 2005.
Publication Year:
2005
Physical Description:
print, 9 ref
Original Material:
INIST-CNRS
Subject Terms:
Electronics, Electronique, Metrology and instrumentation, Métrologie et instrumentation, Optics, Optique, Physics, Physique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Electronique, Electronics, Circuits électriques, optiques et optoélectroniques, Electric, optical and optoelectronic circuits, Réseaux neuronaux, Neural networks, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Modèle dynamique, Dynamic model, Modelo dinámico, Modélisation, Modeling, Modelización, Perceptron multicouche, Multilayer perceptrons, Réseau multicouche, Multilayer network, Red multinivel, Réseau neuronal, Neural network, Red neuronal
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical and Computer Engineering Kielce University of Technology Al. 1000-lecia P.P. 7, 25-314 Kielce, Poland
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17077567
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Electronics
Accession Number:
edscal.17077567
Database:
PASCAL Archive

Further Information

The main aim of the paper is to present application of the artificial neural network in the web traffic prediction. First, the general problem of time series modelling and forecasting is shortly described. Next, the details of building of dynamic processes models with the neural networks are discussed. At this point determination of the model structure in terms of its inputs and outputs is the most important question because this structure is a rough approximation of the dynamics of the modelled process. The following section of the paper presents the results obtained applying artificial neural network (classical multilayer perceptron trained with backpropagation algorithm) to the real-world web traffic prediction. Finally, we discuss the results, describe weak points of presented method and propose some alternative approaches.