Enhanced Network Bandwidth Prediction with Multi-Output Gaussian Process Regression

Le résumé fourni par la source

Modern network environments, especially in do-mains like 5G and IoT, exhibit highly dynamic and nonlinear traffic behaviors, posing significant challenges for accurate time series analysis and predictive modeling. Traditional approaches, including stochastic ARIMA and deep learning-based LSTM, frequently encounter difficulties in capturing rapid signal variations and inter-channel dependencies, often due to data sparsity or excessive computational cost. To address these issues, this paper proposes a Multi-Output Gaussian Process (MOGP) framework augmented with a novel signal processing strategy, where additional signals are generated by summing adjacent elements over multiple window sizes. Such multi-scale enrichment effectively leverages cross-channel correlations, enabling the MOGP model to discover complex temporal patterns in multi-channel data. Experimental results on real-world network traces highlight that the proposed method achieves consistently lower RMSE compared to conventional single-output or deep learning methods, thereby underscoring its value for robust bandwidth estimation. Our findings suggest that integrating MOGP with multi-scale augmentation holds promise for a wide range of predictive analytics applications, including resource allocation in 5G networks and traffic monitoring in IoT systems.

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