International Journal of Engineering and Technology

Today has shown that modern traffic in data networks is highly correlated, making it necessary to select this kind of models that capture autocorrelation characteristics governing data flows surrounding on the network [1]. Being able to perform accurate forecasting of traffic on communication networks, this has great importance at present, since it influences decisions as important such as network sizing and predestination. The main purpose in this paper is to put into context the reader about the importance of statistical models of time series, it enable for estimating future traffic forecasts in modern communications networks, and becomes an essential tool for traffic prediction, This prediction according to the individual needs of each network are listed in estimates with long range dependence (LDR) and short-range dependence (SDR), each one providing a specific control, appropriate and efficient integrated at different levels of the network functional hierarchy [2]. But for the traffic forecasts in the modern communication networks must define the type of network to study and time series model that fits the same, which is why you should first select the type of network. For this case study, is a Wi-Fi network as the traffic behavior requires the development of a time series model with advanced statistics, that allows an integrated observing network and thus provide a tool to facilitate the monitoring and management of the same. According to this the type of time series model to use for this case are the ARIMA time series.