A neural network based time series forecasting system

The neural network (NN) arena has suffered in the past years a remarkable development as one of the novel fields for artificial intelligence (AI). Time series analysis based on models of the variability of observations by postulating trends and cyclic effects, with a view to understand the cause of variation and to improve forecasting, suggests the use of NNs to deal with principal component analysis (PCA). The purpose of this work is to present a logical based NN system, along with: 1) the time series forecasting, with its characteristics of strong noise component and nonlinearity in data, showing itself as a field in which the use of NNs is particularly advisable; and 2) the PCA rules, organized in a default hierarchy as logical theories competing with one another for the right to represent a particular situation or to predict its successors, i.e. assisting in the process of choosing the best network to forecast each series. Some trials are conducted and the basic performance measures are used as baselines for comparison with other methods.