01. Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed

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Browsing 01. Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed by Journal "2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 16 October 2024 through 18 October 2024 -- Ankara -- 204562"

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    Citation - Scopus: 1
    Artificial Intelligence Driven Multivariate Time Series Analysis of Network Traffic Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2024) Filiz, G.; Yıldız, A.; Kara, E.; Altıntaş, S.; Çakar, T.; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
    The 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.