International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Network Traffic Analysis and Prediction: A Comprehensive Review

Author(s) Mr. Robin Thomas, Prof. Mr. D. N. Goswami, Prof. Ms. Anshu Chaturvedi
Country India
Abstract Modern computer networks encounter highly dynamic traffic patterns due to the exponential growth of IoT devices, cloud services, and high-speed 5G networks. Traditional SNMP-based monitoring and packet sampling techniques (NetFlow, sFlow) struggle to capture micro-bursts and unpredictable congestion. To enhance network resource management and Quality of Service (QoS), this study reviews linear time series models (AR, MA, ARIMA), non-linear methods (LSTMs, GARCH), and hybrid approaches. Hybrid models, which integrate statistical forecasting with deep learning techniques, demonstrate superior accuracy in predicting traffic anomalies, congestion, and demand fluctuations. The study evaluates prediction metrics (RMSE, MAPE, NRMSE) and explores challenges such as real-time processing constraints, storage overhead, and model adaptability. Future research should focus on edge computing, federated learning, and SDN-based predictive analytics to improve network efficiency. Our findings indicate that multi-model hybrid architectures provide the best balance between accuracy, scalability, and computational feasibility in modern network environments.
Keywords Network Traffic Analysis, Traffic Prediction Models, Machine Learning in Networking, Time Series Forecasting, Hybrid Prediction Techniques, Real-Time Network Monitoring
Field Computer > Network / Security
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-08
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.41019
Short DOI https://doi.org/g9fb82
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E-ISSN 2582-2160
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