Abstract

The telecommunication is one of the modes of communication, in which most investments are made. It consists of internet, mobile phones, wired and wireless fixed phones, fax, televisions, radio and some other. Among them, demand for internet and cellular phones rapidly increases. For a smooth function of this business, knowledge on demand is much important. Effective forecasts help a business to manage its supply efficiently. This study aimed to find out an accurate mechanism for prediction of demand for internet conections and cellualr phone collections.

Based on the secondary data available in central bank reports from 1996 to 2016, several statistical forecasting models were evaluated for an accurate prediction. There can be seen an increasing demand for both internet and cellular phone connections. Number of internet connections has gone up from 4 110 to 4 921 000, while the usage of cellular phones has developed from 71 228 to 26 228 000 during this period. Rapid growth in internet usage has happened after 2009, while after year 2003, usage of cellular phone has increased rapidly. With compared to models fitted for original form of data, models for log transformed data show better performances. The best performance in prediction of internet connection was given by ARIMA (1,1,1) model fitted for log transformed data, meanwhile ARIMA (0,1,2) model fitted for log transformed data showed the best fit for series of cellular connections. Double exponential smoothing models also show better fit for both series.

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