Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes
This work addresses the need for better extreme value prediction in wireless traffic forecasting to avoid outages and improve energy efficiency, representing an incremental improvement over existing methods.
The paper tackled the problem of forecasting extreme demand spikes and troughs in wireless traffic, which is crucial for network optimization, by designing a feature embedding kernel for Gaussian Processes to improve prediction accuracy and provide probabilistic uncertainty quantification. The result showed a 32% reduction in error for short-term extreme value prediction compared to S-ARIMA and a 17% reduction compared to Naive-GP.
Wireless traffic prediction is a fundamental enabler to proactive network optimisation in beyond 5G. Forecasting extreme demand spikes and troughs due to traffic mobility is essential to avoiding outages and improving energy efficiency. Current state-of-the-art deep learning forecasting methods predominantly focus on overall forecast performance and do not offer probabilistic uncertainty quantification (UQ). Whilst Gaussian Process (GP) models have UQ capability, it is not able to predict extreme values very well. Here, we design a feature embedding (FE) kernel for a GP model to forecast traffic demand with extreme values. Using real 4G base station data, we compare our FE-GP performance against both conventional naive GPs, ARIMA models, as well as demonstrate the UQ output. For short-term extreme value prediction, we demonstrated a 32\% reduction vs. S-ARIMA and 17\% reduction vs. Naive-GP. For long-term average value prediction, we demonstrated a 21\% reduction vs. S-ARIMA and 12\% reduction vs. Naive-GP. The FE kernel also enabled us to create a flexible trade-off between overall forecast accuracy against peak-trough accuracy. The advantage over neural network (e.g. CNN, LSTM) is that the probabilistic forecast uncertainty can inform us of the risk of predictions, as well as the full posterior distribution of the forecast.