Advanced Machine Learning Models for Network Traffic Prediction and Management

Ayodele E. Awokoya, Kayode Olumurewa, Oladapo Adeduro, Kayode Oladapo, Rejoice Omoyen
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:232-243, 2026.

Abstract

This study employs Random Forest, LSTM, and Support Vector Regression (SVR) to forecast network traffic volume at a university. Wireshark was used to capture a full month of campus network traffic. Three models were trained on real-world traffic data and evaluated across short-term (hourly), medium-term (daily), and long-term (weekly) prediction horizons. Across all horizons, LSTM consistently achieved lower RMSE and MAE than Random Forest and SVR, demonstrating its suitability for capturing temporal dependencies in network traffic prediction.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-awokoya26a, title = {Advanced Machine Learning Models for Network Traffic Prediction and Management}, author = {Awokoya, Ayodele E. and Olumurewa, Kayode and Adeduro, Oladapo and Oladapo, Kayode and Omoyen, Rejoice}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {232--243}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/awokoya26a/awokoya26a.pdf}, url = {https://proceedings.mlr.press/v319/awokoya26a.html}, abstract = {This study employs Random Forest, LSTM, and Support Vector Regression (SVR) to forecast network traffic volume at a university. Wireshark was used to capture a full month of campus network traffic. Three models were trained on real-world traffic data and evaluated across short-term (hourly), medium-term (daily), and long-term (weekly) prediction horizons. Across all horizons, LSTM consistently achieved lower RMSE and MAE than Random Forest and SVR, demonstrating its suitability for capturing temporal dependencies in network traffic prediction.} }
Endnote
%0 Conference Paper %T Advanced Machine Learning Models for Network Traffic Prediction and Management %A Ayodele E. Awokoya %A Kayode Olumurewa %A Oladapo Adeduro %A Kayode Oladapo %A Rejoice Omoyen %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-awokoya26a %I PMLR %P 232--243 %U https://proceedings.mlr.press/v319/awokoya26a.html %V 319 %X This study employs Random Forest, LSTM, and Support Vector Regression (SVR) to forecast network traffic volume at a university. Wireshark was used to capture a full month of campus network traffic. Three models were trained on real-world traffic data and evaluated across short-term (hourly), medium-term (daily), and long-term (weekly) prediction horizons. Across all horizons, LSTM consistently achieved lower RMSE and MAE than Random Forest and SVR, demonstrating its suitability for capturing temporal dependencies in network traffic prediction.
APA
Awokoya, A.E., Olumurewa, K., Adeduro, O., Oladapo, K. & Omoyen, R.. (2026). Advanced Machine Learning Models for Network Traffic Prediction and Management. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:232-243 Available from https://proceedings.mlr.press/v319/awokoya26a.html.

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