Prediction of telecommunications market behavior based on LSTM models
Bibliographic Data
| ID | 22189641 |
|---|---|
| Authors | D Ramı́rez (0000-0001-9983-4555, District University of Bogotá), Diego Armando Giral-Ramirez, César Augusto Hernández Suárez (0000-0002-7974-5560, Universidad Distrital Francisco José de Caldas), Cesar Hernández (0000-0001-9409-8341, District University of Bogotá), Cesar Augusto Hernández-Suarez, Luis Fernando Pedraza-Martínez (District University of Bogotá) |
| Year | 2024 |
| Volume | 8 |
| Issue | 15 |
| Pages | 8226 |
| Publication date | 2024-12-16 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Infrastructure Policy and Development (JOURNAL) |
| Journal identifiers | ISSN: 2572-7923 • E-ISSN: 2572-7931 |
| Publisher | EnPress Publisher (PUBLISHER) |
| DOI | 10.24294/jipd8226 |
| OpenAlex | W4405456481 |
| Language | EN |
| References cited | 2 |
The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop
Telecommunications · Computer Science · Customer churn and segmentation · Energy Load and Power Forecasting · Innovation Diffusion and Forecasting
| Highly cited | No |
|---|