Long-term Prediction Method for Cellular Network Traffic Based on EWT and NeuralProphet-MLP
Long term prediction of cellular network traffic is of great significance for network expansion and optimization.To address the many challenges brought by low data availability and nonlinearity in long-term prediction,a decomposition-based frequency division prediction model is proposed.The NeuroalProphet model and multilayer perceptron are used to predict the decomposed low-frequency components and mid-to-high frequency components,and the final results are obtained by applying inverse empirical wavelet transform to the predicted results of each component.Verification is carried on a real cellular network traffic dataset,the results show that the proposed method has a significant improvement in accuracy compared to traditional prediction models and has good application value.
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