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A Hybrid Machine Learning Model for Short-Term Mobile Traffic Forecasting
Abstract
Today, mobile networks are essential for seamless communication. As mobile subscriber numbers increase, mobile network operators must devise strategies to meet the enormous demand for mobile network resources, such as spectrum. Therefore, the need for an effective prediction mechanism for efficient resource management and planning becomes paramount. To address the challenges of non-linearity, burstiness, and hidden temporal patterns in mobile traffic prediction, this paper develops and evaluates a hybrid machine learning model for short-term forecasting of mobile traffic congestion. Real-life base-station mobile data traffic for the first quarter of 2024 from northern and southern Kaduna State, Nigeria, was used. The 40573-row, 10-column entry dataset was preprocessed to remove duplicates, outliers, and missing values. From the processed dataset, 70% was used to train various machine learning models. Long Short-Term Memory (LSTM) and Accelerated Gradient Boosting (AGB), with RMSEs 12% and 15% lower than those of other models, were selected via voting to develop the proposed hybrid prediction model. Thirty per cent (30%) of the processed dataset was then employed to test and evaluate the prediction performance of the proposed model on nine base stations with both the highest and lowest traffic using Root Mean Square (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) as evaluation metrics. From the results, LSTM-AGB outperformed LSTM and ARIMA, achieving the lowest RMSE, MAPE, and MAE values of 2354, 8.04%, and 196, for both uplink and downlink prediction evaluations. These findings demonstrate the effectiveness of the proposed model for short-term mobile traffic prediction by enabling proactive congestion management through early identification of imminent traffic surges, thereby enabling preemptive adjustments to resource allocation and network planning. The proposed hybrid model provides a scalable, data-driven approach that can support the efficient operation of modern mobile stations, especially in resource-constrained environments.
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DOI: https://doi.org/10.47738/jads.v7i4.1121
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