VoLTE service volume prediction method and terminal equipment

Abstract

The invention discloses a VoLTE service traffic prediction method and terminal equipment, and the method comprises the steps: determining the temporal distribution data of the historical service traffic of a VoLTE service, and enabling the temporal distribution data to be used for representing the distribution condition of the historical service traffic in a time dimension; based on the temporal distribution data, training a long and short time memory network LSTM model and an integrated moving average autoregressive ARIMA model; and fitting the LSTM model and the ARIMA model to obtain a fitting model, so as to predict the VoLTE traffic of the target time based on the fitting model. Therefore, the LSTM model and the ARIMA model are trained based on the temporal distribution data of the historical service volume of the VoLTE service, and the VoLTE service volume of the target time is predicted based on the fitting model obtained after fitting of the LSTM model and the ARIMA model, so that the purpose of more accurately predicting the service volume of the VoLTE service can be achieved.

Description

technical field [0001] The present invention relates to the field of terminals, in particular to a VoLTE service traffic prediction method and terminal equipment. Background technique [0002] Voice over Long-Term Evolution (VoLTE) is a high-speed wireless communication standard for mobile phones and data terminals, which enables voice services (control and media layers) to be transmitted as data streams in the LTE data bearer network instead of No more maintenance and reliance on traditional circuit-switched voice networks. [0003] Existing methods for forecasting the traffic volume of VoLTE services mainly include forecasting based on the number of terminal equipment connected to the network, and forecasting based on the VoLTE traffic volume and human experience of the same major holidays in the past; however, when the VoLTE traffic volume is predicted by the above method There are problems with inaccurate predictions. [0004] Therefore, there is an urgent need for a m...

AI Technical Summary

Problems solved by technology

[0003] Existing methods for forecasting the traffic volume of VoLTE services mainly include forecasting based on the number of terminal equipment connected to the network, and forecasting based on the VoLTE traffic volume and human experience of the same major holidays in the past; however, when the VoLTE traffic volume is predicted by the above method There are problems with inaccurate predictions

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