IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i9p290-d1226717.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/9/290/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/9/290/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    2. João A. Bastos, 2019. "Forecasting the capacity of mobile networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(2), pages 231-242, October.
    3. 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.
    4. Evgeni Mokrov & Konstantin Samouylov, 2023. "Performance Assessment and Comparison of Deployment Options for 5G Millimeter Wave Systems," Future Internet, MDPI, vol. 15(2), pages 1-12, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matteo Mogliani, 2010. "Residual-based tests for cointegration and multiple deterministic structural breaks: A Monte Carlo study," Working Papers halshs-00564897, HAL.
    2. Shahbaz, Muhammad & Hoang, Thi Hong Van & Mahalik, Mantu Kumar & Roubaud, David, 2017. "Energy consumption, financial development and economic growth in India: New evidence from a nonlinear and asymmetric analysis," Energy Economics, Elsevier, vol. 63(C), pages 199-212.
    3. Growitsch Christian & Nepal Rabindra & Stronzik Marcus, 2015. "Price Convergence and Information Efficiency in German Natural Gas Markets," German Economic Review, De Gruyter, vol. 16(1), pages 87-103, February.
    4. Lee, Chi-Chuan & Lee, Chien-Chiang & Ning, Shao-Lin, 2017. "Dynamic relationship of oil price shocks and country risks," Energy Economics, Elsevier, vol. 66(C), pages 571-581.
    5. Antonia López Villavicencio & Josep Lluís Raymond Bara, 2006. "The short and long-run determinants of the real exchange rate in Mexico," Working Papers wpdea0606, Department of Applied Economics at Universitat Autonoma of Barcelona.
    6. Raphaël Chiappini & Dominique Torre & Elise Tosi, 2019. "Romania's Unsustainable Stabilization: 1929-1933," GREDEG Working Papers 2019-43, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    7. Guili Liao & Qimeng Liu & Rongmao Zhang & Shifang Zhang, 2022. "Rank test of unit‐root hypothesis with AR‐GARCH errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(5), pages 695-719, September.
    8. Saaed, A.A.J., 2007. "Inflation and Economic Growth in Kuwait: 1985-2005. Evidence from Co-Integration and Error Correction Model," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 7(1).
    9. Demiralay, Sercan & Ulusoy, Veysel, 2014. "Value-at-risk Predictions of Precious Metals with Long Memory Volatility Models," MPRA Paper 53229, University Library of Munich, Germany.
    10. Zanin, Luca & Marra, Giampiero, 2012. "Assessing the functional relationship between CO2 emissions and economic development using an additive mixed model approach," Economic Modelling, Elsevier, vol. 29(4), pages 1328-1337.
    11. John Barkoulas & Christopher Baum & Mustafa Caglayan, 1999. "Fractional monetary dynamics," Applied Economics, Taylor & Francis Journals, vol. 31(11), pages 1393-1400.
    12. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2017. "Do oil price asymmetric effects on the stock market persist in multiple time horizons?," Applied Energy, Elsevier, vol. 185(P2), pages 1799-1808.
    13. Bahmani-Oskooee, Mohsen & Bohl, Martin T., 2000. "German monetary unification and the stability of the German M3 money demand function," Economics Letters, Elsevier, vol. 66(2), pages 203-208, February.
    14. Xiaojie Xu, 2017. "The rolling causal structure between the Chinese stock index and futures," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(4), pages 491-509, November.
    15. Kevin S. Nell & Maria M. De Mello, 2019. "The interdependence between the saving rate and technology across regimes: evidence from South Africa," Empirical Economics, Springer, vol. 56(1), pages 269-300, January.
    16. repec:kap:iaecre:v:17:y:2011:i:2:p:157-168 is not listed on IDEAS
    17. Nikeel Kumar & Ronald Ravinesh Kumar & Radika Kumar & Peter Josef Stauvermann, 2020. "Is the tourism–growth relationship asymmetric in the Cook Islands? Evidence from NARDL cointegration and causality tests," Tourism Economics, , vol. 26(4), pages 658-681, June.
    18. Jan Babecký & Fabrizio Coricelli & Roman Horváth, 2009. "Assessing Inflation Persistence: Micro Evidence on an Inflation Targeting Economy," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 59(2), pages 102-127, June.
    19. Creel, Jerome & Bihan, Herve Le, 2006. "Using structural balance data to test the fiscal theory of the price level: Some international evidence," Journal of Macroeconomics, Elsevier, vol. 28(2), pages 338-360, June.
    20. Matteo Pelagatti & Emilio Colombo, 2012. "Unpuzzling the Purchasing Power Parity Puzzle," Working Papers 221, University of Milano-Bicocca, Department of Economics, revised Mar 2012.
    21. Turvey, Calum G., 2001. "Random Walks And Fractal Structures In Agricultural Commodity Futures Prices," Working Papers 34151, University of Guelph, Department of Food, Agricultural and Resource Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:15:y:2023:i:9:p:290-:d:1226717. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.