Traffic data prediction of mobile communication base station based on wavelet neural network

With the wide application of new media, users require more and more mobile communication. In order to satisfy users' high-quality experience and save resources, it is necessary to predict the traffic data of mobile communication base station, so that mobile communication base station can adjust the frequency load quantity according to the traffic fluctuation. From March 1 to April 9, 2018, this paper collects traffic data, selects 40, 000 sets of data, uses python to mine data, and predicts the traffic data of mobile communication base station by establishing wavelet neural network short-time traffic prediction model. The results show that the average accuracy of the short-term prediction model is 43. 15 and the root mean square error is 0. 0076.