# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 5 Conclusions  
The number of users and equipment is growing extremely quickly, and telecom operators need to understand the demand for different types of applications in next-generation networks. The ability to predict such demand would help service providers make better offers to customers. This paper has explored the use of statistical methods of data analysis in the context of 5G networks. The main objective of this study was to analyze mobile network traffic and develop forecasting models for traffic profiles. Two statistical models, SARIMA and Holt-Winters, were constructed and evaluated for this purpose. The results demonstrate that both models effectively predict the average values of upload and download traffic within a certain range. However, it was observed that the Holt-Winters model is better suited for forecasting download traffic profiles, while SARIMA is more suitable for upload traffic profiles.  
From our numerical analysis, we found that each statistical method has its own specifications. There is no universality, as each dataset requires its own approach. For example, the MAPE for download traffic was 11.2% for SARIMA and 15% for Holt-Winters. However, the Holt-Winters model was better suited for upload traffic, with a MAPE of 4.17% compared to 9.9% for SARIMA and Holt-Winters, respectively. Additionally, we observed that the MSE metric for download traffic was 86 times less for the Holt-Winters model (0.00021) compared to SARIMA (0.0181). Conversely, for upload traffic, the MSE was almost four times less for SARIMA (0.00004) compared to Holt-Winters (0.0015).  
Future studies will focus on combining statistical models with machine learning methods for more precise forecasts, as well as anomaly detection. By implementing such techniques, we aim to enhance the accuracy and reliability of traffic forecasting in 5G networks. These findings contribute to the growing body of knowledge surrounding the utilization of data analysis methods in the field of telecommunications.  
Conceptualization, project administration, supervision, methodology, writing--review and editing, I.K. and A.G.; formal analysis, investigation, A.K. and I.K.; software, validation, visualization, writing--original draft, A.K. and S.B. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Absolute error for traffic forecast.  
**Funding:** This publication has been supported by the RUDN University Scientific Projects Grant System, project No. 025319-2-000 (recipient [PERSON]). The research by [PERSON] has been supported by the Ministry of Education and Science of the Russian Federation as part of the program of the Moscow Center for Fundamental and Applied Mathematics under the agreement No. 075-15-2022-284. The research was carried out using the infrastructure of the Shared Research Facilities \"High Performance Computing and Big Data\" (CKP \"Informatics\") of the Federal Research Center \"Computer Science and Control\" of the Russian Academy of Sciences.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors are sincerely grateful to [PERSON] (IST/INESC-ID, University of Lisbon) for providing the dataset.  
**Conflicts of Interest:** The authors declare no conflict of interest.