Please use this identifier to cite or link to this item: http://repository.kln.ac.lk/handle/123456789/22428
Title: Modelling and Forecasting the Usage of Cellular and Landline Phones in Sri Lanka: Univariate Time Series Approach
Authors: Karunarathner, A.W.S.P.
Perera, M.S.H.
Liyanage, U.P.
Keywords: AIC, ARIMA, MAPE, ACF, PACF
Issue Date: 2020
Publisher: International Journal of Academic Research
Citation: Karunarathner, A.W.S.P. , Perera, M.S.H. and Liyanage, U.P. ( 2020). Modelling and Forecasting the Usage of Cellular and Landline Phones in Sri Lanka: Univariate Time Series Approach, International Journal of Academic Research, Volume 02, Issue 01, December 2020.pp.41
Abstract: Phones have become a mandatory commodity in human life. Nowadays, there is a very strong increase in the cellular phone market, so we tend to forget landline phone services. According to statistics, cellular phones and landline phones usage up to December 2018 is 32,528,104 and 2,484, 616 respectively. That is, the teledensity (per 100 inhabitants) is 150 for cellular phones and 11.5 for landline phones. Due to the increment of the cellular phones and decrement of the landline phones, it is vitally important to study their behaviour. Therefore, the objective of this paper is to model and forecast the usage of cellular and landline phones in Sri Lanka. The model was developed using 80% of the data and validated with 20%. The usage was modelled with Autoregressive Integrated Moving Average (ARIMA) technique. Several models were fitted and based on the lowest Akaike’s Information Criteria (AIC), ARIMA (1,2,1) and ARIMA (2,2,1) were identified as the best-fitted models with forecasting accuracy measured by Mean Absolute Percentage Error (MAPE) values 1.403 and 0.976 for cellular and landline phones usage respectively, concluding that two ARIMA models have a strong potential for forecasting the usage of cellular and landline phones. This model would be important to those who are with the telecoms market to achieve their business goals.
URI: http://repository.kln.ac.lk/handle/123456789/22428
ISSN: 2706-0292
Appears in Collections:IJAR 2020

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