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Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models

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  • Irina Kochetkova

    (Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia
    Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilova St., 119333 Moscow, Russia)

  • Anna Kushchazli

    (Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia)

  • Sofia Burtseva

    (Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia)

  • Andrey Gorshenin

    (Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilova St., 119333 Moscow, Russia)

Abstract

Fifth-generation (5G) networks require efficient radio resource management (RRM) which should dynamically adapt to the current network load and user needs. Monitoring and forecasting network performance requirements and metrics helps with this task. One of the parameters that highly influences radio resource management is the profile of user traffic generated by various 5G applications. Forecasting such mobile network profiles helps with numerous RRM tasks such as network slicing and load balancing. In this paper, we analyze a dataset from a mobile network operator in Portugal that contains information about volumes of traffic in download and upload directions in one-hour time slots. We apply two statistical models for forecasting download and upload traffic profiles, namely, seasonal autoregressive integrated moving average (SARIMA) and Holt-Winters models. We demonstrate that both models are suitable for forecasting mobile network traffic. Nevertheless, the SARIMA model is more appropriate for download traffic (e.g., MAPE [mean absolute percentage error] of 11.2% vs. 15% for Holt-Winters), while the Holt-Winters model is better suited for upload traffic (e.g., MAPE of 4.17% vs. 9.9% for SARIMA and Holt-Winters, respectively).

Suggested Citation

  • Irina Kochetkova & Anna Kushchazli & Sofia Burtseva & Andrey Gorshenin, 2023. "Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models," Future Internet, MDPI, vol. 15(9), pages 1-15, August.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:9:p:290-:d:1226717
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    References listed on IDEAS

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    4. Andrey Gorshenin & Victor Kuzmin, 2022. "Statistical Feature Construction for Forecasting Accuracy Increase and Its Applications in Neural Network Based Analysis," Mathematics, MDPI, vol. 10(4), pages 1-21, February.
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