Long term 5G network traffic forecasting via modeling non-stationarity with deep learning

Abstract 5G cellular networks have recently fostered a wide range of emerging applications, but their popularity has led to traffic growth that far outpaces network expansion. This mismatch may decrease network quality and cause severe performance problems. To reduce the risk, operators need long term traffic prediction to perform network expansion schemes months ahead. However, long term prediction horizon exposes the non-stationarity of series data, which deteriorates the performance of existing approaches. We deal with this problem by developing a deep learning model, Diviner, that incorporates stationary processes into a well-designed hierarchical structure and models non-stationary time series with multi-scale stable features. We demonstrate substantial performance improvement of Diviner over the current state of the art in 5G network traffic forecasting with detailed months-level forecasting for massive ports with complex flow patterns. Extensive experiments further present its applicability to various predictive scenarios without any modification, showing potential to address broader engineering problems.