1. Home
  2. Vol 8, No 6 (2024)
  3. Juhana

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

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

Abstract


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


Keywords


Base Station Traffic; 4G/LTE; Forecasting; GLMnet; ARIMA; Prophet.

References


Thanawala, R., El-Sayed, M., Morawski, T., Mukhopadhyay, A., Zhao, J., & Urrutia-Valdés, C. (2010). The mobile data explosion and new approaches to network planning and monetization. Proceedings of 2010 14th International Telecommunications Network Strategy and Planning Symposium, Networks, Warsaw, Poland. doi:10.1109/NETWKS.2010.5624926.

Jiang, W. (2022). Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications, 201(April), 117163. doi:10.1016/j.eswa.2022.117163.

Li, R., Zhao, Z., Zheng, J., Mei, C., Cai, Y., & Zhang, H. (2017). The Learning and Prediction of Application-Level Traffic Data in Cellular Networks. IEEE Transactions on Wireless Communications, 16(6), 3899–3912. doi:10.1109/TWC.2017.2689772.

Capozzi, F., Piro, G., Grieco, L. A., Boggia, G., & Camarda, P. (2012). On accurate simulations of LTE femtocells using an open source simulator. Eurasip Journal on Wireless Communications and Networking, 1-13. doi:10.1186/1687-1499-2012-328.

Zheng, Y. L., Zhang, L. P., Zhang, X. L., Wang, K., & Zheng, Y. J. (2015). Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PLoS ONE, 10(3), 116832. doi:10.1371/journal.pone.0116832.

Liu, Q., Liu, X., Jiang, B., & Yang, W. (2011). Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC Infectious Diseases, 11(1), 1-7. doi:10.1186/1471-2334-11-218.

Kumar, S. V., & Vanajakshi, L. (2015). Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review, 7(3), 1-9. doi:10.1007/s12544-015-0170-8.

Hjort Kure, E. H., Engelstad, P., Maharjan, S., Gjessing, S., Zhang, X., & Zhang, Y. (2018). Energy Usage Forecasting for LTE: A Network-Wide Traffic Measurements Study. IEEE Globecom Workshops, GC Wkshps 2018 – Proceedings, Abu Dhabi, United Arab Emirates. doi:10.1109/GLOCOMW.2018.8644423.

Lo Schiavo, L., Fiore, M., Gramaglia, M., Banchs, A., & Costa-Perez, X. (2022). Forecasting for Network Management with Joint Statistical Modelling and Machine Learning. In Proceedings - IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2022, 60–69. doi:10.1109/WoWMoM54355.2022.00028.

Zhu, X., Yang, Z., Liu, G., Li, Y., Xu, L., Cheng, X., & Dong, R. (2022, July). Cell Expansion Priority Recommendation Based on Prophet Algorithm. In Signal and Information Processing, Networking and Computers: Proceedings of the 8th International Conference on Signal and Information Processing, Networking and Computers (ICSINC), 1449-1457. doi:10.1007/978-981-19-3387-5_172.

Jain, G., & Prasad, R. R. (2020). Machine learning, Prophet and XGBoost algorithm: Analysis of Traffic Forecasting in Telecom Networks with time series data. ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), 893–897. doi:10.1109/ICRITO48877.2020.9197864.

Dou, R., & Meng, F. (2022). Prophet-LSTM combination model carbon trading price prediction research. Proceedings Volume 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022); 124741W. doi:10.1117/12.2653785.

Duarte, D., & Faerman, J. (2019). Comparison of Time Series Prediction of Healthcare Emergency Department Indicators with ARIMA and Prophet, 123–133. doi:10.5121/csit.2019.91810.

Kochetkova, I., Kushchazli, A., Burtseva, S., & Gorshenin, A. (2023). Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models. Future Internet, 15(9), 1–15. doi:10.3390/fi15090290.

Dongchen, Z., Shoufeng, W., Xiaoyan, X., Xingzheng, L., Wenwen, Y., & Tinglan, W. (2014). A novel long term traffic forecast algorithm and case study for china. Proceedings - 2014 IEEE 28th International Conference on Advanced Information Networking and Applications Workshops, IEEE WAINA 2014, 425–430. doi:10.1109/WAINA.2014.69.

Gu, J., Ruan, Y., Chen, X., & Wang, C. (2012). A novel traffic capacity planning methodology for LTE radio network dimensioning. IET Conference Publications, 2011(586 CP), 462–466. doi:10.1049/cp.2011.0711.

Cho, C., & Lee, S. (2012). Mobile broadband traffic forecasts in Korea. In DCNET 2012, ICE-B 2012, OPTICS 2012 - Proceedings of the International Conference on Data Communication Networking, e-Business and Optical Communication Systems, ICETE2012, 41–45. doi:10.5220/0004066200410045.

Yu, L., Li, M., Jin, W., Guo, Y., Wang, Q., Yan, F., & Li, P. (2021). STEP: A Spatio-Temporal Fine-Granular User Traffic Prediction System for Cellular Networks. IEEE Transactions on Mobile Computing, 20(12), 3453–3466. doi:10.1109/TMC.2020.3001225.

Rizwan, A., Arshad, K., Fioranelli, F., Imran, A., & Imran, M. A. (2018). Mobile Internet Activity Estimation and Analysis at High Granularity: SVR Model Approach. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC (Vols. 2018-September), Bologna, Italy. doi:10.1109/PIMRC.2018.8581040.

Huang, C. W., & Chen, P. C. (2020). Joint demand forecasting and DQN-Based control for energy-aware mobile traffic offloading. IEEE Access, 8, 66588–66597. doi:10.1109/ACCESS.2020.2985679.

Nayak, N., & R, R. S. (2021). 5G Traffic Prediction with Time Series Analysis. International Journal of Innovative Technology and Exploring Engineering, 10(12), 36–40. doi:10.35940/ijitee.l9555.10101221.

Köppelová, J., & Jindrová, A. (2019). Application of exponential smoothing models and ARIMA models in time series analysis from telco area. Agris On-Line Papers in Economics and Informatics, 11(3), 73–84. doi:10.7160/aol.2019.110307.

Mahdy, B., Abbas, H., Hassanein, H. S., Noureldin, A., & Abou-Zeid, H. (2020). A clustering-driven approach to predict the traffic load of mobile networks for the analysis of base stations deployment. Journal of Sensor and Actuator Networks, 9(4), 53. doi:10.3390/jsan9040053.

Lv, T., Wu, Y., & Zhang, L. (2021). A Traffic Interval Prediction Method Based on ARIMA. Journal of Physics: Conference Series, 1880(1). doi:10.1088/1742-6596/1880/1/012031.

Lee, D., Lee, D., Choi, M., & Lee, J. (2020). Prediction of Network Throughput using ARIMA. 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, 1–5. doi:10.1109/ICAIIC48513.2020.9065083.

Suarez, C. A. H., Parra, O. J. S., & Díaz, A. E. (2009). An ARIMA model for forecasting Wi-Fi data network traffic values. Ingenieria e Investigacion, 29(2), 65–69. doi:10.15446/ing.investig.v29n2.15163.

Huang, C. W., Chiang, C. T., & Li, Q. (2017). A study of deep learning networks on mobile traffic forecasting. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2017-October, 1–6. doi:10.1109/PIMRC.2017.8292737.

Prajam, S., Wechtaisong, C., & Khan, A. A. (2022). Applying Machine Learning Approaches for Network Traffic Forecasting. Indian Journal of Computer Science and Engineering, 13(2), 324–335. doi:10.21817/indjcse/2022/v13i2/221302188.

Zheng, J., & Huang, M. (2020). Traffic flow forecast through time series analysis based on deep learning. IEEE Access, 8, 82562–82570. doi:10.1109/ACCESS.2020.2990738.

Kaur, G., Grewal, S. K., & Jain, A. (2024). Federated Learning Based Spatio-Temporal Framework for Real-Time Traffic Prediction. Wireless Personal Communications, 136(2), 849–865. doi:10.1007/s11277-024-11292-z.

Yang, S., Wu, J., Du, Y., He, Y., & Chen, X. (2017). Ensemble Learning for Short-Term Traffic Prediction Based on Gradient Boosting Machine. Journal of Sensors, 2017, 1–15. doi:10.1155/2017/7074143.

Wu, Q., Chen, X., Zhou, Z., Chen, L., & Zhang, J. (2021). Deep reinforcement learning with spatio-temporal traffic forecasting for data-driven base station sleep control. IEEE/ACM2021 Transactions on Networking, 29(2), 935–948. doi:10.1109/TNET.2021.3053771.

Zhao, J., Qu, H., Zhao, J., Dai, H., & Jiang, D. (2020). Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction. Transactions on Emerging Telecommunications Technologies, 31(11), e4056. doi:10.1002/ett.4056.

Zhao, L., Huang, Y., Wang, Y., Xu, Y., Feng, Q., & Chen, E. (2023). Base Station Traffic Prediction based on Feature Selection and Stacking Ensemble Learning. ACM International Conference Proceeding Series, 113–117. doi:10.1145/3603781.3603800.

Sudhakaran, S., Venkatagiri, A., Taukari, P. A., Jeganathan, A., & Muthuchidambaranathan, P. (2020). Metropolitan Cellular Traffic Prediction Using Deep Learning Techniques. 2020 IEEE International Conference on Communication, Networks and Satellite, Comnetsat 2020 - Proceedings, 106–110. doi:10.1109/Comnetsat50391.2020.9328937.

Clemente, D., Soares, G., Fernandes, D., Cortesao, R., Sebastiao, P., & Ferreira, L. S. (2019). Traffic forecast in mobile networks: Classification system using machine learning. IEEE Vehicular Technology Conference, 2019-September, Honolulu, United States. doi:10.1109/VTCFall.2019.8891348.

Fang, W. X., Lan, P. C., Lin, W. R., Chang, H. C., Chang, H. Y., & Wang, Y. H. (2019). Combine Facebook Prophet and LSTM with BPNN Forecasting financial markets: The Morgan Taiwan Index. Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019, 4–5. doi:10.1109/ISPACS48206.2019.8986377.

Wang, H., Liu, S., Sun, C., Li, Z., & Jiang, X. (2022). Short term prediction of cell uplink and downlink traffic based on Prophet algorithm. ACM International Conference Proceeding Series, 419–423. doi:10.1145/3558819.3565118.

Xiao, H., Zhao, Y., & Zhang, H. (2023). Predict Vessel Traffic with Weather Conditions Based on Multimodal Deep Learning. Journal of Marine Science and Engineering, 11(1), 39. doi:10.3390/jmse11010039.

Tan, H. (2023). A Novel Approach to Wireless Network Traffic Prediction using CNN-LSTM Attention Model with Prophet Model. Proceedings - 2023 2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023, MLCCIM 2023, 285–292. doi:10.1109/MLCCIM60412.2023.00047.

Wei, X., Liu, Z., Li, M., & He, X. (2023). Application of Prophet Model in Traffic Matrix Prediction for IP Backbone Network. 2023 8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023, 136–140. doi:10.1109/ICSP58490.2023.10248446.

Mohammadjafari, S., Roginsky, S., Kavurmacioglu, E., Cevik, M., Ethier, J., & Bener, A. B. (2020). Machine Learning-Based Radio Coverage Prediction in Urban Environments. IEEE Transactions on Network and Service Management, 17(4), 2117–2130. doi:10.1109/TNSM.2020.3035442.

Azari, A., Papapetrou, P., Denic, S., & Peters, G. (2019). Cellular Traffic Prediction and Classification: A Comparative Evaluation of LSTM and ARIMA. In P. Kralj Novak, T. Šmuc, & S. Džeroski (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 11828 LNAI, 129–144. doi:10.1007/978-3-030-33778-0_11.

Santos Escriche, E., Vassaki, S., & Peters, G. (2023). A comparative study of cellular traffic prediction mechanisms. Wireless Networks, 29(5), 2371–2389. doi:10.1007/s11276-023-03313-9.

Mehri, H., Chen, H., & Mehrpouyan, H. (2024). Cellular Traffic Prediction Using Online Prediction Algorithms. arXiv Preprint arXiv:2405.05239. doi:10.48550/arXiv.2405.05239.

Perifanis, V., Pavlidis, N., Koutsiamanis, R. A., & Efraimidis, P. S. (2023). Federated learning for 5G base station traffic forecasting. Computer Networks, 235, 1389–1286. doi:10.1016/j.comnet.2023.109950.

Wang, Y. (2022). Base Station Mobile Traffic Prediction Based on ARIMA and LSTM Model. Lecture Notes in Electrical Engineering: Vol. 797 LNEE, 164–175. doi:10.1007/978-981-16-5692-7_18.

Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2018). A Comparison of ARIMA and LSTM in Forecasting Time Series. Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, 1394–1401. doi:10.1109/ICMLA.2018.00227.

Xu, M., Wang, Q., & Lin, Q. (2018). Hybrid holiday traffic predictions in cellular networks. IEEE/IFIP Network Operations and Management Symposium: Cognitive Management in a Cyber World, NOMS 2018, 1–6. doi:10.1109/NOMS.2018.8406291.

Bangroo, R., Verma, S. R., Shivangi, & Shakuntala. (2023). Comparative Study of Elastic Net Regression, Naive Bayes & Lasso Regression. IEEE International Conference on Electrical, Electronics, Communication and Computers, ELEXCOM 2023, 1–6. doi:10.1109/ELEXCOM58812.2023.10370433.

Garg, R., & Barpanda, S. (2022). Machine learning algorithms for time series analysis and forecasting. arXiv preprint arXiv:2211.14387. doi:10.48550/arXiv.2211.14387.


Full Text: PDF

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

Refbacks

  • There are currently no refbacks.