Application of convolutional neural networks for mobile internet forecasting in Colombia

Luis Fernando Pedraza, Cesar Augusto Hernandez, Ernesto Cadena

Article ID: 10283
Vol 9, Issue 1, 2025

VIEWS - 30 (Abstract)

Abstract


The growth of mobile Internet has facilitated access to information by minimizing geographical barriers. For this reason, this paper forecasts the number of users, incomes, and traffic for operators with the most significant penetration in the mobile internet market in Colombia to analyze their market growth. For the forecast, the convolutional neural network (CNN) technique is used, combined with the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit (GRU) techniques. The CNN training data corresponds to the last twelve years. The results currently show a high concentration in the market since a company has a large part of the market; however, the forecasts show a decrease in its users and revenues and the growth of part of the competition. It is also concluded that the technique with the most precision in the forecasts is CNN-GRU.


Keywords


artificial intelligence; competition; forecast; mobile internet; telecommunications market

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DOI: https://doi.org/10.24294/jipd10283

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