Modelling the multi-layer artificial neural network for internet traffic forecasting: The model selection design issues

Internet traffic forecasting models with learning ability, such as
the artificial neural network (ANN), have been growing in
popularity in recent time due to their impressive performance in
modelling the high degree of variability and nonlinearity of
internet traffic. This study examined the impacts of some design
issues on performance of the multi-layer artificial neural network
for internet traffic forecasting. The traffic forecasting was
modelled as a standard time series problem and the multilayer
artificial neural network designed to performs the time series
function mapping. The input lags were varied from 1 to 24. The
training epoch values of 200, 500, and 1000 on one and two
hidden layered networks were used. The learning algorithm was
backpropagation with 0.1 learning rate and 0.9 momentum on
logistic sigmoid activation function. The model was implemented
in Visual Basic and validated with four categories of classified
time series internet traffic of a branch residential network of one
of a firm in Nigeria. Various predictive performances without
consistent pattern were observed on the issues considered,
however, input lag one gave the worst performance in all cases for
the HOURLY traffic; three of the four traffic categories
demonstrated the superiority of two hidden layers to one hidden
layer. Although the epoch values of 200, 500 and 1000 showed no
consistent performance variations, epoch value 200 outperformed
the others on the model selections. The study revealed that input
lags, number of hidden layers and epoch values could impact on
the traffic forecasting performance of multilayer perceptron and
that performance could be considerably improved by careful
selection of those parameters through experimentations.