Modeling Economic Time Series Using a Focused Time Lagged FeedForward Neural Network
N. Moseley · 2003
ABSTRACT,- Artificial neural networks (ANN) are simplified mathematical representations of some aspects of the functioning of the human brain. ANN’s based on the Multilayer Perceptron (MLP) the base architecture of layered networks, have been shown to be a powerful tool for input-output mapping and have been used extensively in many disciplines. In this paper we demonstrate the use of a neural network to model univariate economic time series,. For this thesis, a MLP based network simulator was designed and implemented in the C programming language. Specifically a Time Focused Feed forward layered network (TFLN) trained with standard back propagation algorithm with momentum is the chosen architecture. Focused Time Lagged Feed Forward Networks acquire temporal processing ability through the realization of short-term memory. The neural network generates estimates of the time series after training, additionally the ability of the network to discover nonlinear relationships was used to investigate the interaction between two key economic indicators from their representation as total sales and total inventories time series. A model validation regime predicated digital signal processing methodology was developed. The results of these studies demonstrate that the application of neural networks to time series data seems to hold promise as an effective tool for analysis and forecasting. 1.