Filiz, GozdeAltıntaş, SuatYıldız, AyşenurKara, ErkanDrias, YassineÇakar, Tuna2025-10-052025-10-0520259798331566555https://doi.org/10.1109/SIU66497.2025.11112468https://hdl.handle.net/20.500.11779/3101Isik 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.trinfo:eu-repo/semantics/closedAccessDeep Learning ModelsInternet Traffic PredictionTime Series AnalysisXGBoostComputational EfficiencyData MiningDeep LearningForecastingIntelligent SystemsLearning SystemsRegression AnalysisDeep Learning ModelHigh-AccuracyInternet TrafficLearning ModelsPerformanceTime-Series AnalysisTraffic PredictionTraffic SpeedXgboostConference Object10.1109/SIU66497.2025.111124682-s2.0-105015414240