Comparing forecasting approaches for Internet traffic

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：Internet traffic,Long memory time series,Nonlinear time series,FARIMA models,Neural networks,Hybrid models

论文评审过程：Available online 25 June 2015, Version of Record 17 July 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.06.029