Authors : Choukri Benhamed; Slimane Mekaoui

Volume/Issue : Volume 11 - 2026, Issue 9 - September

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Google Scholar : https://tinyurl.com/y9phnsb3

DOI : https://doi.org/10.38124/ijisrt/26sep342

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : The rapid expansion of Internet-based services, including cloud computing, video streaming, IoT, and emerging 5G/6G applications, has increased both the volume and complexity of core IP network traffic. Accurate short-term traffic forecasting is essential for capacity planning, congestion control, and quality of service assurance. Core IP traffic exhibits non-stationarity, burstiness, and time-varying variance, which limits the effectiveness of traditional ARIMA models that assume constant residual variance. This paper proposes a hybrid ARIMA–GARCH framework, where ARIMA models the conditional mean and GARCH captures residual volatility and heteroscedasticity.

Keywords : IP Traffic Forecasting, ARIMA, GARCH.

References :

1. V. Rajalakshmi, S. Ganesh “Hybrid Time-Series Forecasting Models for Traffic Flow Prediction” https://doi.org/10.7307/ptt.v34i4.3998
2. M. Raja M. Rajkumar,  “Traffic flow forecasting using support vector machine and comparing prediction accuracy with decision tree” in  AIP Conference. Procedings. 3267, 020101 (2025) https://doi.org/10.1063/5.0270504
3. Priya Vij, Ankita Tiwari, ” Improving Urban Traffic Management with Big Data-Driven Forecasting Models Using ARIMA and Prophet Algorithms”2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI) DOI: 10.1109/ICCSAI64074.2025.11064187
4. L. Zhao, X.Wen, Y Wang, Y Shao “A novel hybrid model of ARIMA-MCC and CKDE-GARCH for urban short-term traffic flow prediction” in IET Intelligent Transport …, 2022 - Wiley Online Library DOI: 10.1049/itr2.12138
5. R Yao, W Zhang, L Zhang : “Hybrid methods for short-term traffic flow prediction based on ARIMA-GARCH model and wavelet neural network ” in Journal of Transportation Engineering …, 2020 - Vol. 146, No. 8 https://doi.org/10.1061/JTEPBS.00003
6. I.Kochetkova, A.Kushchazli, S.Burtseva, A Gorshenin, “Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models” in Future Internet 2023, 15(9), 290; https://doi.org/10.3390/fi15090290
7. [7] S. Abdulla Alblooshi; M. Masmoudi; A. Cheaitou; K. Hamad, “Predicting Metro Ridership in Dubai: Analyzing Seasonal Trends with SARIMA, Holt-Winters, and LSTM” 2024 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD) DOI: 10.1109/ICTMOD63116.2024.10878219
8. J Ou, X Huang, Y Zhou, Z Zhou, Q Nie : “ Traffic volatility forecasting using an omnibus family GARCH modeling framework” in Entropy  journal (2022,)24(10), 1392; https://doi.org/10.3390/e24101392
9. M Ali, KM Yusof, B Wilson:” Traffic speed prediction using GARCH‐GRU hybrid model” in IET Intelligent Transport (2023) DOI: 10.1049/itr2.12411
10. M.KIM: “Network traffic prediction based on INGARCH model” in Wireless Networks journal, 2020 – Springer volume 26, pages 6189–6202, (2020) https://doi.org/10.1007/s11276-020-02431-y
11. M.Amiri & L. Mohammad-Khanli, (2017). Survey on prediction models of applications for resources provisioning in Cloud. Journal of Network and Computer Applications 2017, 82, 93–113.https://doi.org/10.1016/j.jnca.2017.01.016
12. Dong-wei Dong-wei Xu Yong-dong Wang Li-min Jia Yong Qin Hong-hui Dong “Real-time road traffic state prediction based on ARIMA and Kalman filter” Frontiers of Information Technology Electronic Engineering February 2017, Volume 18, Issue 2,