Modified Hybrid Grey Model (1,1) to Forecast Cellular Subscribers

This study develops MHGM (1,1) (Modified Hybrid Grey Model) which is the combination of two models first one is improved GM (1,1), this model consists of optimization of initial and background values and other is concave EDDGM (1,1) (Dynamic Discrete Grey Model) termed, in this model equal division...

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Main Authors: Maria Junejo, Asif Ali Shaikh, Abdul Sami Qureshi
Format: Article
Language:English
Published: Mehran University of Engineering and Technology 2018-04-01
Series:Mehran University Research Journal of Engineering and Technology
Online Access:http://publications.muet.edu.pk/index.php/muetrj/article/view/241
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spelling doaj-c263edfaa85442c497b131352a4688672020-11-24T23:02:07ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192018-04-0137243944410.22581/muet1982.1802.19241Modified Hybrid Grey Model (1,1) to Forecast Cellular SubscribersMaria Junejo0Asif Ali Shaikh1Abdul Sami Qureshi2Department of Basic Sciences and Related Studies, Mehran University of Engineering and Technology, JamshoroDepartment of Basic Sciences and Related Studies, Mehran University of Engineering and Technology, JamshoroDepartment of Civil Engineering, Mehran University of Engineering and Technology, JamshoroThis study develops MHGM (1,1) (Modified Hybrid Grey Model) which is the combination of two models first one is improved GM (1,1), this model consists of optimization of initial and background values and other is concave EDDGM (1,1) (Dynamic Discrete Grey Model) termed, in this model equal division technique is applied to fit the concavity of cumulative sequence and after that created dynamic average value and on the basis of that dynamic average value dynamic discrete GM (1,1) model is established and by the gradual heuristics method or the dichotomy approach the initial equal division number is obtained. We have fixed equal division number ‘n’ between 0 and 1in MHGM (1,1). For forecasting of starting half years we use y(0)(m) as initial condition of model in time restored function and also multiply by a factor e-b 1 to adjust the model. This model has applied without solving by heuristics or dichotomy method. Subscribers of cellular networks increase day by day in Pakistan; cellular industry has total five networks in Pakistan. In this paper data of three cellular networks subscribers that are Mobilink, Ufone and Zong have taken as application of models and it has been proved by using mean absolute percentage error that the forecast accuracy of MHGM (1,1) is better than GM (1,1) (Grey Model) and improved grey model (1,1).http://publications.muet.edu.pk/index.php/muetrj/article/view/241
collection DOAJ
language English
format Article
sources DOAJ
author Maria Junejo
Asif Ali Shaikh
Abdul Sami Qureshi
spellingShingle Maria Junejo
Asif Ali Shaikh
Abdul Sami Qureshi
Modified Hybrid Grey Model (1,1) to Forecast Cellular Subscribers
Mehran University Research Journal of Engineering and Technology
author_facet Maria Junejo
Asif Ali Shaikh
Abdul Sami Qureshi
author_sort Maria Junejo
title Modified Hybrid Grey Model (1,1) to Forecast Cellular Subscribers
title_short Modified Hybrid Grey Model (1,1) to Forecast Cellular Subscribers
title_full Modified Hybrid Grey Model (1,1) to Forecast Cellular Subscribers
title_fullStr Modified Hybrid Grey Model (1,1) to Forecast Cellular Subscribers
title_full_unstemmed Modified Hybrid Grey Model (1,1) to Forecast Cellular Subscribers
title_sort modified hybrid grey model (1,1) to forecast cellular subscribers
publisher Mehran University of Engineering and Technology
series Mehran University Research Journal of Engineering and Technology
issn 0254-7821
2413-7219
publishDate 2018-04-01
description This study develops MHGM (1,1) (Modified Hybrid Grey Model) which is the combination of two models first one is improved GM (1,1), this model consists of optimization of initial and background values and other is concave EDDGM (1,1) (Dynamic Discrete Grey Model) termed, in this model equal division technique is applied to fit the concavity of cumulative sequence and after that created dynamic average value and on the basis of that dynamic average value dynamic discrete GM (1,1) model is established and by the gradual heuristics method or the dichotomy approach the initial equal division number is obtained. We have fixed equal division number ‘n’ between 0 and 1in MHGM (1,1). For forecasting of starting half years we use y(0)(m) as initial condition of model in time restored function and also multiply by a factor e-b 1 to adjust the model. This model has applied without solving by heuristics or dichotomy method. Subscribers of cellular networks increase day by day in Pakistan; cellular industry has total five networks in Pakistan. In this paper data of three cellular networks subscribers that are Mobilink, Ufone and Zong have taken as application of models and it has been proved by using mean absolute percentage error that the forecast accuracy of MHGM (1,1) is better than GM (1,1) (Grey Model) and improved grey model (1,1).
url http://publications.muet.edu.pk/index.php/muetrj/article/view/241
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