IP Traffic Forecasting Using Focused Time Delay Feed Forward Neural Network

Abstract

There have been a number of methods presented by various researchers for traffic prediction, some of which involve modeling the problem of traffic
prediction as a time series. It has been observed that Artificial Neural Networks (ANN) perform better than statistical methods for time series forecasting. The
network performance and complexity varies with the choice of algorithm used. Back propagation (BPNN) has been used to predict IP traffic with a fair degree of
accuracy but as the prediction interval increases and the inputs change drastically the forecasting accuracy suffers [9]. This paper discusses the use of Focused
Time Delay Feed Forward Neural Network architecture to predict IP traffic patterns and overcome the short comings of back propagation neural networks when
used for traffic forecasting along with improvements to the BPNN by using additional inputs like holidays and maintenance downtimes.