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Mobile communication base station traffic forecast

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DOI: 10.23977/cpcs.2021.51009 | Downloads: 4 | Views: 97


Dongxiao Liu 1, Wei Li 1


1 North China University of Technology, Beijing, 100043

Corresponding Author

Dongxiao Liu


This paper studies the prediction problem based on the historical time series data of the base station. Established LSTM and ARIMA models to train and predict traffic data respectively. This paper choose to put the model species into each time series, and put the forecast results into the data table according to the best. At the same time, we found that the traffic data of the cell showed three patterns of rising, steady, and falling over time, instead of rising all the time. Our LSTM model uses the ‘elu’ function as the activation function, the number of training rounds is 50, the number of neurons is 1000, and the observation and output sequence is 120, which is closer to the true value in short-term prediction. The ARIMA model retains the trend of the original model and has achieved better results in long-term forecasting.


LSTM model, ARIMA model, long-term forecasting


Dongxiao Liu, Wei Li. Mobile communication base station traffic forecast. Computing, Performance and Communication Systems (2021) Vol. 5: 52-55. DOI:


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