Comparing forecasting approaches for Internet traffic
Internet traffic is modeled using time series and neural network approaches. FARIMA and ANNs are combined in two different ways for better predictions. A framework for comparison of the different approaches is introduced. Forecasting with a model selected based on non-linearity test is a successful strategy. Alternatively, hybridization between MLP and FARIMA is found to be equally effective.
internet traffic neural networks long memory time series nonlinear time series farima models hybrid models