Cellular Traffic Prediction and Classification: a comparative evaluation of LSTM and ARIMA
This work addresses cellular network resource utilization by evaluating standard methods, but it is incremental as it applies existing techniques to a real dataset without introducing new algorithms.
The paper compared LSTM and ARIMA for cellular traffic prediction and classification, finding that LSTM generally outperforms ARIMA, especially with sufficient training data and feature selection, while ARIMA performs nearly optimally in some cases with lower complexity.
Prediction of user traffic in cellular networks has attracted profound attention for improving resource utilization. In this paper, we study the problem of network traffic traffic prediction and classification by employing standard machine learning and statistical learning time series prediction methods, including long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA), respectively. We present an extensive experimental evaluation of the designed tools over a real network traffic dataset. Within this analysis, we explore the impact of different parameters to the effectiveness of the predictions. We further extend our analysis to the problem of network traffic classification and prediction of traffic bursts. The results, on the one hand, demonstrate superior performance of LSTM over ARIMA in general, especially when the length of the training time series is high enough, and it is augmented by a wisely-selected set of features. On the other hand, the results shed light on the circumstances in which, ARIMA performs close to the optimal with lower complexity.