Comparative evaluation of ARIMA and ANFIS for modeling of wireless network traffic time series

Network traffic modeling significantly affects various considerations in networking, including network resource allocation, quality of service provisioning, network traffic management, congestion control, and bandwidth efficiency. These are very important issues in network protocol design, too. In this paper, a comprehensive comparison of modeling approaches of adaptive neuro fuzzy inference system (ANFIS) and autoregressive integrated moving average (ARIMA) for modeling of wireless network traffic in terms of typical statistical indicator and computational complexity has been attempted. ARIMA has been widely used in this area for past many years. On the other hand, ANFIS is comparatively new, and no network traffic modeling using ANFIS was attempted until recently to the best of our knowledge. At the same time, a detailed comparative performance evaluation of ANFIS with other modeling approaches in traffic modeling could not be found in existing literature. Reportedly, ANFIS provides a good precision in prediction in terms of statistical indicators and also gives effective description of network conditions at different times. However, the computational complexity of ANFIS for traffic modeling is a major concern and deserves a closer inspection. In our case of wireless network traffic, as a final result, we find that ANFIS model performs better than the best ARIMA model in three different scenarios.