Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting

Tutun Juhana; Hajiar Yuliana; . Hendrawan; . Iskandar; Yasuo Musashi

doi:10.28991/ESJ-2024-08-06-04

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Tutun Juhana School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132,, Indonesia
Hajiar Yuliana
hajiar.yuliana@lecture.unjani.ac.id
School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132,, Indonesia https://orcid.org/0009-0008-9157-3520
. Hendrawan School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132,, Indonesia
. Iskandar School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132,, Indonesia
Yasuo Musashi Research and Education Institute for Semiconductors and Informatics, Kumamoto University, Kumamoto 860-8555,, Japan

Vol. 8 No. 6 (2024): December

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This study evaluates the performance of three forecasting models”ARIMA, Prophet, and Glmnet”with the primary objective of equipping the telecommunication industry with effective tools for cellular traffic forecasting. These tools lay the foundation for efficient resource management, cost optimization, and enhanced service delivery. The study begins with dataset description and preparation, followed by the selection of traffic forecasting models, and concludes with performance evaluation based on metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The main contribution of this research is a comprehensive comparison of the three forecasting methods, aiding practitioners and researchers in identifying the best prediction model for specific contexts. The findings reveal that Glmnet consistently outperforms ARIMA and Prophet across all categories of traffic forecasting on the selected performance metrics. Its ability to handle complex data structures, manage multicollinearity, and deliver robust and accurate predictions makes it the preferred choice for forecasting cellular network traffic in the telecommunications domain.

Doi: 10.28991/ESJ-2024-08-06-04

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Juhana, T., Yuliana, H., Hendrawan, ., Iskandar, ., & Musashi, Y. (2024). Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting. Emerging Science Journal, 8(6), 2197–2217. https://doi.org/10.28991/ESJ-2024-08-06-04

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Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting

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Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting

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