Método automático híbrido SSA-ARIMA-NEURAL para previsão multi-step de séries temporais estocásticas

Abstract

The development of forecasting methods fundamentally aims to reduce the uncertainty inherent in predicting non-deterministic future events. It is necessary because the information about the future behavior of variables allows a better planning of situations to come, independent of what they are. For that, using the idea that real world time series are neither pure linear nor non-linear but instead a combination of those, a new hybrid automatic method called "SSA-ARIMA-Neural" is proposed. This method consists in decomposing the original time series by means of Singular Spectrum Analysis and forecasting each component not classified as noise independently. The trend component, which is understood as more purely linear, is modeled by the Box-Jenkins methodology and the oscillatory components, in turn, having non-linear behavior, are approximated by Artificial Neural Networks. In the end, all independent forecasts are summed, generating the final prediction. Aiming at validate this method, a computational experiment was performed using a data set obtained from instrumentation of a large concrete gravitational dam, where the resulting forecasts where compared with the ones generated using consecrated methods from the literature. Those comparisons showed a relevant improvement.