Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting
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 optimizatio...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ital Publication
2024-12-01
|
| Series: | Emerging Science Journal |
| Subjects: | |
| Online Access: | https://ijournalse.org/index.php/ESJ/article/view/2588 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850136083297206272 |
|---|---|
| author | Tutun Juhana Hajiar Yuliana . Hendrawan . Iskandar Yasuo Musashi |
| author_facet | Tutun Juhana Hajiar Yuliana . Hendrawan . Iskandar Yasuo Musashi |
| author_sort | Tutun Juhana |
| collection | DOAJ |
| description | 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
Full Text: PDF |
| format | Article |
| id | doaj-art-7066a3ec8c154281bc5c6edb1bdc6863 |
| institution | OA Journals |
| issn | 2610-9182 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj-art-7066a3ec8c154281bc5c6edb1bdc68632025-08-20T02:31:13ZengItal PublicationEmerging Science Journal2610-91822024-12-01862197221710.28991/ESJ-2024-08-06-04735Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic ForecastingTutun Juhana0Hajiar Yuliana1. Hendrawan2. Iskandar3Yasuo Musashi4School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132,School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132,School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132,School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132,Research and Education Institute for Semiconductors and Informatics, Kumamoto University, Kumamoto 860-8555,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 Full Text: PDFhttps://ijournalse.org/index.php/ESJ/article/view/2588base station traffic4g/lteforecastingglmnetarimaprophet. |
| spellingShingle | Tutun Juhana Hajiar Yuliana . Hendrawan . Iskandar Yasuo Musashi Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting Emerging Science Journal base station traffic 4g/lte forecasting glmnet arima prophet. |
| title | Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting |
| title_full | Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting |
| title_fullStr | Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting |
| title_full_unstemmed | Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting |
| title_short | Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting |
| title_sort | comparative analysis of arima prophet and glmnet for long term evolution lte base station traffic forecasting |
| topic | base station traffic 4g/lte forecasting glmnet arima prophet. |
| url | https://ijournalse.org/index.php/ESJ/article/view/2588 |
| work_keys_str_mv | AT tutunjuhana comparativeanalysisofarimaprophetandglmnetforlongtermevolutionltebasestationtrafficforecasting AT hajiaryuliana comparativeanalysisofarimaprophetandglmnetforlongtermevolutionltebasestationtrafficforecasting AT hendrawan comparativeanalysisofarimaprophetandglmnetforlongtermevolutionltebasestationtrafficforecasting AT iskandar comparativeanalysisofarimaprophetandglmnetforlongtermevolutionltebasestationtrafficforecasting AT yasuomusashi comparativeanalysisofarimaprophetandglmnetforlongtermevolutionltebasestationtrafficforecasting |