Failure to meet the demand for e-banking services with the necessary infrastructure can cause many problems for a society and the process of economic activity in a society. Therefore, for this type of service forecasting the changes in demand is important to provide the infrastructure to meet the demand. The main purpose of this paper is forecasting the demand for e-banking services using two methods, artificial neural networks and SARIMA models, comparing two methods and volume of demand with infrastructures in Iran. This research sample consists of 88 observed transactions through 6 current channels of the banking network since Tir 1385 to Mehr 1392. Also by using these models, the demand is forecasted to the end of Aban 1393. The results indicate a continuing increasing trend and also show advantage of artificial neural networks to SARIMA model. Therefore, serious attention to infrastructure of electronic banking services is essential.
Sharzehi, G., & Ghaffari Nejad, A. H. (2014). Forecasting Demand for Electronic Banking Services in Iran using Artificial Neural Networks and SARIMA Methods. Quarterly Journal of Applied Theories of Economics, 1(3), 107-130.
MLA
Gholamali Sharzehi; Amir Hossein Ghaffari Nejad. "Forecasting Demand for Electronic Banking Services in Iran using Artificial Neural Networks and SARIMA Methods". Quarterly Journal of Applied Theories of Economics, 1, 3, 2014, 107-130.
HARVARD
Sharzehi, G., Ghaffari Nejad, A. H. (2014). 'Forecasting Demand for Electronic Banking Services in Iran using Artificial Neural Networks and SARIMA Methods', Quarterly Journal of Applied Theories of Economics, 1(3), pp. 107-130.
VANCOUVER
Sharzehi, G., Ghaffari Nejad, A. H. Forecasting Demand for Electronic Banking Services in Iran using Artificial Neural Networks and SARIMA Methods. Quarterly Journal of Applied Theories of Economics, 2014; 1(3): 107-130.
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