Development of New Models for Network Traffic Forecasting - Couverture souple

Akg�l, Derman ; Akay, Mehmet Fatih

9783330022492: Development of New Models for Network Traffic Forecasting

Couverture souple

ISBN 10 : 3330022493 ISBN 13 : 9783330022492

Editeur : LAP LAMBERT Academic Publishing, 2016

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The purpose of this work is to forecast the amount of network traffic in Transmission Control Protocol/Internet Protocol (TCP/IP) -based networks by using different time lags and various machine learning methods including Support Vector Machines (SVM), Multilayer Perceptron (MLP), Radial Basis Function (RBF) Neural Network, M5P (a decision tree with linear regression functions at the nodes), Random Forest (RF), Random Tree (RT), and Reduced Error Prunning Error (REPTree), and statistical regression methods including Multiple Linear Regression (MLR) and Holt-Winters and compare the performance of statistical and machine learning methods. Two different Internet Service Providers' (ISPs) traffic data have been utilized to build traffic forecasting models. The first 66% of the data sets has been utilized as training sets and the rest has been used as test sets. The performance of the forecasting models for the data sets has been assessed using Mean Absulote Percentage Error (MAPE). The results show that SVM and M5P based models usually perform better than the ones obtained by the other methods.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

� propos de l?auteur

Derman AKG�L was born in Antakya-Turkey, in 1986. She graduated from the Department of Mathematics, �ukurova University, Adana, in 2008 and started the MSc program at Department of Mathematics, University Cincinnati, Ohio, USA in 2010 and graduated in 2012. She completed MSc program at the Department of Computer Engineering in 2016.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2016
Langue: anglais
ISBN 10: 3330022493
ISBN 13: 9783330022492
Reliure: Broch�
Nombre de pages: 112
Coordonn�es du fabricant: non disponible
Personne responsable: KreGa Onlinehandel GmbH
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Development of New Models for Network Traffic Forecasting

Derman Akg�l

Edit� par LAP Lambert Academic Publishing Jan 2017, 2017

ISBN 10 : 3330022493 ISBN 13 : 9783330022492

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The purpose of this work is to forecast the amount of network traffic in Transmission Control Protocol/Internet Protocol (TCP/IP) -based networks by using different time lags and various machine learning methods including Support Vector Machines (SVM), Multilayer Perceptron (MLP), Radial Basis Function (RBF) Neural Network, M5P (a decision tree with linear regression functions at the nodes), Random Forest (RF), Random Tree (RT), and Reduced Error Prunning Error (REPTree), and statistical regression methods including Multiple Linear Regression (MLR) and Holt-Winters and compare the performance of statistical and machine learning methods. Two different Internet Service Providers' (ISPs) traffic data have been utilized to build traffic forecasting models. The first 66% of the data sets has been utilized as training sets and the rest has been used as test sets. The performance of the forecasting models for the data sets has been assessed using Mean Absulote Percentage Error (MAPE). The results show that SVM and M5P based models usually perform better than the ones obtained by the other methods. 112 pp. Englisch. N� de r�f. du vendeur 9783330022492

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Development of New Models for Network Traffic Forecasting

Derman Akg�l|Mehmet Fatih Akay

Edit� par LAP LAMBERT Academic Publishing, 2016

ISBN 10 : 3330022493 ISBN 13 : 9783330022492

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Development of New Models for Network Traffic Forecasting

Derman Akg�l

Edit� par LAP LAMBERT Academic Publishing Jan 2017, 2017

ISBN 10 : 3330022493 ISBN 13 : 9783330022492

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -The purpose of this work is to forecast the amount of network traffic in Transmission Control Protocol/Internet Protocol (TCP/IP) -based networks by using different time lags and various machine learning methods including Support Vector Machines (SVM), Multilayer Perceptron (MLP), Radial Basis Function (RBF) Neural Network, M5P (a decision tree with linear regression functions at the nodes), Random Forest (RF), Random Tree (RT), and Reduced Error Prunning Error (REPTree), and statistical regression methods including Multiple Linear Regression (MLR) and Holt-Winters and compare the performance of statistical and machine learning methods. Two different Internet Service Providers' (ISPs) traffic data have been utilized to build traffic forecasting models. The first 66% of the data sets has been utilized as training sets and the rest has been used as test sets. The performance of the forecasting models for the data sets has been assessed using Mean Absulote Percentage Error (MAPE). The results show that SVM and M5P based models usually perform better than the ones obtained by the other methods.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 112 pp. Englisch. N� de r�f. du vendeur 9783330022492

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Development of New Models for Network Traffic Forecasting

Derman Akg�l

Edit� par LAP Lambert Academic Publishing, 2016

ISBN 10 : 3330022493 ISBN 13 : 9783330022492

Neuf Taschenbuch

impression � la demande

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The purpose of this work is to forecast the amount of network traffic in Transmission Control Protocol/Internet Protocol (TCP/IP) -based networks by using different time lags and various machine learning methods including Support Vector Machines (SVM), Multilayer Perceptron (MLP), Radial Basis Function (RBF) Neural Network, M5P (a decision tree with linear regression functions at the nodes), Random Forest (RF), Random Tree (RT), and Reduced Error Prunning Error (REPTree), and statistical regression methods including Multiple Linear Regression (MLR) and Holt-Winters and compare the performance of statistical and machine learning methods. Two different Internet Service Providers' (ISPs) traffic data have been utilized to build traffic forecasting models. The first 66% of the data sets has been utilized as training sets and the rest has been used as test sets. The performance of the forecasting models for the data sets has been assessed using Mean Absulote Percentage Error (MAPE). The results show that SVM and M5P based models usually perform better than the ones obtained by the other methods. N� de r�f. du vendeur 9783330022492

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