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Accurate network traffic prediction of base station cell is very vital forthe expansion and reduction of wireless devices in base station cell. The burstand uncertainty of base station cell network traffic makes the network trafficnonlinear and non-stationary, which brings challenges to the long-termprediction of network traffic. In this paper, the traffic model LMA-DeepAR forbase station network is established based on DeepAR. Acordding to thedistribution characteristics of network traffic, this paper proposes anartificial feature sequence calculation method based on local moving average(LMA). The feature sequence is input into DeepAR as covariant, which makes thestatistical characteristics of network traffic near a period of time in thepast be considered when updating parameters, and the interference ofnon-stationary network traffic on model training will be reduced. Experimentalresults show that the proposed prediction approach (LMA-DeepAR) outperformsother methods in the overall long-term prediction performance and stability ofmulti cell network traffic.

Base Station Network Traffic Prediction Approach Based on LMA-DeepAR