Forecasting Mobile Network Traffic Data using Coactive Adaptive Neuro-Fuzzy Inference System Model

The analysis of 2G and 3G voice traffic by researchers have established that their characteristics follow the Poisson distribution and that earlier theories on call arrivals hold. However, few research work have been conducted in the literature, with respect to predicting 2G and 3G voice traffic using artificial intelligent networks. This study explores the forecasting capabilities of CANFIS model in predicting voice traffic of two different mobile network generations: 2G as first input data while 3G serve as second input data. This study uses 3G weekly voice traffic and 2G weekly voice traffic time series data measured from a live mobile network with nationwide coverage between 2015 and 2017 in Ghana. The results indicate that CANFIS model with Bell membership function, 7 membership function per input, TanhAxon transfer function and Levenberg-Marquardt learning rule can give accurate traffic prediction for 3G voice traffic. With 2G voice traffic, CANFIS model with Gaussian membership function, 5 membership function per input, Axon transfer function and momentum learning rule was the best model. The results indicate that CANFIS model can be used to predict both 2G and 3G weekly voice traffic with appreciable improvement.

The authors declare no conflict of interest.

Not applicable

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.