Filiz, G.Yıldız, A.Kara, E.Altıntaş, S.Çakar, T.2025-02-052025-02-0520249798350379433https://doi.org/10.1109/ASYU62119.2024.10756993https://hdl.handle.net/20.500.11779/2496IEEE SMC; IEEE Turkiye SectionThe primary objective of this research is to employ artificial intelligence, machine learning, and neural networks in order to construct a network traffic prediction model. The analysis of network traffic data obtained from a digital media and entertainment provider operating in Turkey is conducted through the application of multivariate time-series analysis techniques in order to get insights into the temporal patterns and trends. In model development, Vector Autoregression (VAR), Vector Error Correction Model (VECM), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) algorithms have been utilized. LSTM and GRU models have performed better with low Mean Absolute Percentage Error (MAPE) and high R-squared Score (R2). LSTM model has reached 0.98 R2 and 8.95% MAPE. These results indicate that the models can be utilized in network management optimization as resource allocation, congestion detection, anomaly detection, and quality of service. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessArtificial IntelligenceLong-Short Term MemoryMachine LearningNetwork Traffic ForecastingNeural NetworksConference Object10.1109/ASYU62119.2024.107569932-s2.0-85213347300