Research Article
Development of a Hybrid Prediction Mechanism using SMA and EXS Methods for GSM Logical Channel Load Variables
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 109 - Issue 1 |
| Published: January 2015 |
| Authors: Garba S, Mu'azu M.B, Dajab D.D |
10.5120/19151-0577
|
Garba S, Mu'azu M.B, Dajab D.D . Development of a Hybrid Prediction Mechanism using SMA and EXS Methods for GSM Logical Channel Load Variables. International Journal of Computer Applications. 109, 1 (January 2015), 16-24. DOI=10.5120/19151-0577
@article{ 10.5120/19151-0577,
author = { Garba S,Mu'azu M.B,Dajab D.D },
title = { Development of a Hybrid Prediction Mechanism using SMA and EXS Methods for GSM Logical Channel Load Variables },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 109 },
number = { 1 },
pages = { 16-24 },
doi = { 10.5120/19151-0577 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Garba S
%A Mu'azu M.B
%A Dajab D.D
%T Development of a Hybrid Prediction Mechanism using SMA and EXS Methods for GSM Logical Channel Load Variables%T
%J International Journal of Computer Applications
%V 109
%N 1
%P 16-24
%R 10.5120/19151-0577
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The GSM logical channel load are stochastic (random), distinct in time (Erlang) distribution data; and as such it requires robust means of its prediction. The method employed in this work for the predictions is a hybrid of Simple Moving Average (SMA) and Exponential Smoothing (ExS), which can fit in to predict logical channel load variables with it peculiarities. A three (3) month Data were used in determining the number of observations for the prediction (n) for SMA and smoothing constant (?) for ExS. The determinant values obtained are n = 28, and ? = 0. 077. These values are used to predict the logical control and traffic channels load variables that characterizes its utilization.
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