On The Use Of Quasi-Newton Based Training Of A Feed Forward Neural Network For Time Series Forecasting

This paper examines the effectiveness of using a quasi-Newton based training of a feedforward neural network for forecasting. We have developed a novel quasi-Newton based training algorithm using a generalized logistic function. We have shown that a well designed feed forward structure can lead to a good forecast without the use of the more complicated feedback /feedforward structure of the recurrent network. keywords: Feed forward neural network, quasi-Newton, forecasting 1 Introduction Many time series have significant chaotic components like short time fluctuations (seasonal variations and cyclical fluctuations), random fluctuations, and long time fluctuations (trend). Stochastic model building and forecasting is one of the techniques for the analysis of discrete time series in the time-domain. Autoregressive Integrated Moving Average (ARIMA) models are examples of these statistical models. These models have largely been linear and as such are not able to capture trends accurately..