Abstract
Because of the burstiness and uncertainty of network, the prediction for short-term network traffic is a difficult problem. This paper proposes a real-time network traffic prediction model based on Long Short-Term Memory (LSTM) neural network. The loss function of LSTM network is modified to enhance the robustness of the prediction model. Different from the traditional LSTM model, the proposed model is continually updated with the arrival of new traffic. The experimental results show that the proposed model performs better on prediction accuracy than other models constructed with Support Vector Regression and Back Propagation neural network.
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