On the problem of forecasting air pollutant concentration with morphological models

In this paper we present a study about the time series generator phenomenon from air pollutant concentration forecasting problems. It provides several evidences suggesting that this kind of generator phenomenon has nonlinear characteristics with long-term dependencies. Based on that, we present a nonlinear morphological model able to forecast these time series. Also, a descending gradient-based method, using ideas from the back-propagation algorithm, is presented to design the proposed model. Furthermore, an empirical analysis is conducted with the proposed model using a set of six relevant air pollutant concentration forecasting problems and employing three statistical measures to assess forecasting performance.