Forecasting for Network Management with Joint Statistical Modelling and Machine Learning

Forecasting is a task of ever increasing importance for the operation of mobile networks, where it supports anticipatory decisions by network intelligence and enables emerging zerotouch service and network management models. While current trends in forecasting for anticipatory networking lean towards the systematic adoption of models that are purely based on deep learning approaches, we pave the way for a different strategy to the design of predictors for mobile network environments. Specifically, following recent advances in time series prediction, we consider a hybrid approach that blends statistical modelling and machine learning by means of a joint training process of the two methods. By tailoring this mixed forecasting engine to the specific requirements of network traffic demands, we develop a Thresholded Exponential Smoothing and Recurrent Neural Network (TES-RNN) model. We experiment with TESRNN in two practical network management use cases, i.e., (i) anticipatory allocation of network resources, and (ii) mobile traffic anomaly prediction. Results obtained with extensive traffic workloads collected in an operational mobile network show that TES-RNN can yield substantial performance gains over current state-of-the-art predictors in both applications considered.

Automated summary

Forecasting is becoming increasingly important for mobile network operations, enabling anticipatory decisions and supporting zerotouch service and network management models. This research presents a hybrid approach that combines statistical modeling and machine learning for predictor design in mobile networks. Experimental results demonstrate that the new model outperforms current state-of-the-art predictors in network resource allocation and mobile traffic anomaly prediction.