Mobile network traffic prediction based on machine learning
- 10.2991/978-94-6463-098-5_191How to use a DOI?
- base station traffic prediction; Time series prediction; ARIMA model; LSTM model
In order to better cope with the overall network efficiency and energy consumption caused by the tide phenomenon of the network traffic of the base station and the physical capacity expansion caused by the increasing network traffic demand, we need to predict the network traffic of the base station in real time, so as to guide the design of the time-sharing switching program of the base station and provide suggestions for future planning and construction. In this paper, ARIMA model and LSTM model are used to predict the base station traffic respectively, and RMSE is used as the model evaluation index. The experimental results show that the deviation RMSE predicted by ARIMA model is 1.904, and the deviation RMSE predicted by LSTM model is 1.993. Therefore, ARIMA model predicts more accurately, that is, it performs better in predicting the base station traffic data of ARIMA model.
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Cite this article
TY - CONF AU - Huike Shi PY - 2022 DA - 2022/12/27 TI - Mobile network traffic prediction based on machine learning BT - Proceedings of the 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022) PB - Atlantis Press SP - 1691 EP - 1698 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-098-5_191 DO - 10.2991/978-94-6463-098-5_191 ID - Shi2022 ER -