Building neural network forecasting models from time series ARIMA models: A procedure and a comparative analysis

摘要

A procedure for designing a multilayer perceptron for repdicting time series is proposed. It is based on the generation, according to a set of rules emerging from an ARIMA model previously fitted, of a set of nonlinear forecasting models. These rules are extracted from the set of non-zero coefficients in the ARIMA model, so they consider the autcorrelation structure of the time series. The proposed procedure is intended to help the user in the task of specifying as simple models as possible, providing an unambiguous methodology to construct neural networks for time series forecasting. The performance of this procedure is empirically studied by means of a comparative analysis involving time series from three domains.