Filiz, GozdeAltintas, SuatYildiz, AysenurKara, ErkanDrias, YassineCakar, Tuna2025-10-052025-10-052025979833156656297983315665552165-0608https://doi.org/10.1109/SIU66497.2025.11112468Isik UniversityThis study addresses the prediction of internet traffic speed using time-dependent data from an internet service provider through different modeling approaches. On an anonymized dataset, the performance of the moving average method, various deep learning models (N-BEATS, N-HITS, TimesNet, TSMixer, LSTM), and the XGBoost regression model enhanced with feature engineering was compared. Time series cross-validation and random hyperparameter search were used for model training. According to the results, the XGBoost model achieved the highest accuracy with 98.7% explained variance (R2), while among the deep learning models, N-BEATS and N-HITS achieved the best performance with R2 values around 90%. The findings indicate that tree-based methods supported by carefully selected features can offer higher accuracy and computational efficiency compared to complex deep learning models in internet traffic forecasting. © 2025 Elsevier B.V., All rights reserved.This study addresses the prediction of internet traffic speed using time-dependent data from an internet service provider through different modeling approaches. On an anonymized dataset, the performance of the moving average method, various deep learning models (N-BEATS, N-HITS, TimesNet, TSMixer, LSTM), and the XGBoost regression model enhanced with feature engineering was compared. Time series cross-validation and random hyperparameter search were used for model training. According to the results, the XGBoost model achieved the highest accuracy with 98.7% explained variance (R-2), while among the deep learning models, N-BEATS and N-HITS achieved the best performance with R-2 values around 90%. The findings indicate that tree-based methods supported by carefully selected features can offer higher accuracy and computational efficiency compared to complex deep learning models in internet traffic forecasting.trinfo:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/closedAccessInternet Traffic PredictionTime Series AnalysisDeep Learning ModelsXgboostConference Object10.1109/SIU66497.2025.111124682-s2.0-105015414240