Time Series Analysis of High Speed Wireless Networks: Forecasting network load using time series models - Couverture souple

Raza, S. Akhter; Aqil Burney, S. M.

 
9783846524688: Time Series Analysis of High Speed Wireless Networks: Forecasting network load using time series models
Couverture souple
ISBN 10 : 3846524689 ISBN 13 : 9783846524688
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

Modeling and forecasting of network traffic data presents a number of challenges in recent paradigm due to the volatility of data. There are various methods used for forecasting time series including AR, MA, ARMA, ARIMA, Fourier transform, ANN, and fuzzy logic. Wavelets technique has attracted the attention of researchers and is a rapidly growing area of research. Using the wavelet transformation, a multiresolution representation of a traffic signal is possible which breaks the signal into its shifted and scaled versions. This breaking up of signal is used for smoothing of time series to differentiate between signal and noise. The filtered and de-noised data is then further used to search time series models as possible candidates for forecasting. These models may be standard AR, ANN or fuzzy, etc to produce forecast that best estimate the mean and variance of actual traffic. Wavelets based Seasonal autoregressive moving average model is used to forecasting network traffic load at University of Karachi’s High Speed Fibre Optics LAN which supports Wireless Computing. The new forecasting strategy incredibly improves the performance.

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Présentation de l'éditeur

Modeling and forecasting of network traffic data presents a number of challenges in recent paradigm due to the volatility of data. There are various methods used for forecasting time series including AR, MA, ARMA, ARIMA, Fourier transform, ANN, and fuzzy logic. Wavelets technique has attracted the attention of researchers and is a rapidly growing area of research. Using the wavelet transformation, a multiresolution representation of a traffic signal is possible which breaks the signal into its shifted and scaled versions. This breaking up of signal is used for smoothing of time series to differentiate between signal and noise. The filtered and de-noised data is then further used to search time series models as possible candidates for forecasting. These models may be standard AR, ANN or fuzzy, etc to produce forecast that best estimate the mean and variance of actual traffic. Wavelets based Seasonal autoregressive moving average model is used to forecasting network traffic load at University of Karachi’s High Speed Fibre Optics LAN which supports Wireless Computing. The new forecasting strategy incredibly improves the performance.

Biographie de l'auteur

Dr. Syed Akhter Raza received his Ph.D. from University of Karachi in the year 2011 and having about 20 years teaching experience at various institutions. His research interests include time series modeling, soft computing, data mining, statistics, and simulation of stochastic processes. He is a senior member of IACSIT.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
3846524689
ISBN 13 
9783846524688
Reliure
Broché
Nombre de pages
156
Coordonnées du fabricant
non disponible
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