Abstract
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).
Highlights
Now-a-days it is easy to make and receive a call at any time and any place by the advent of mobiles phone
Portable computers can directly connect to a cellular network mostly for those people who travel a lot
This study develops MHGM (1,1) which is combination of two models, background value is taken from improved GM (1,1) and the value of restored function is taken from EDDGM (1,1) and the value of ‘n’ which is equal division number is fixed between 0 and 1 with the help of ‘r’ value which represents number of years the model will be forecast without solving by heuristics or dichotomy method
Summary
Now-a-days it is easy to make and receive a call at any time and any place by the advent of mobiles phone. It was studied AGM (1,1) (Adaptive Grey Model)for the solution of wafer level packaging process and showed that this process give effective results with small data and it can be improved wafer level packaging process [6], and it was explained that through perturbation bound parameters of GM (1,1) will change large when sample size of sample is large, so if sequence greater than or equal to zero, checking of quasi smooth and exponential condition is satisfied by input sequence the original GM can achieve good prediction the samples will be large [7]. That was put to forecast the growth rate of renewable energy consumption in China, in this work three model that is GM (1,1), nonlinear grey Bernoulli (1,1) and grey verhulst model is compared with each other and it can be show that grey verhulst model has greater accuracy than the two model the accuracy and fitness of models are compared by regression analysis [11]
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