Pronóstico de series de tiempo con tendencia y ciclo estacional usando el modelo airline y redes neuronales artificiales

many time series with trend and seasonal pattern are successfully modeled and forecasted by the airline model of box and jenkins; however, this model neglects the presence of nonlinearity on data. in this paper, we propose a new nonlinear version of the airline model; for this, we replace the moving average linear component by a multilayer perceptron neural network. the proposed model is used for forecasting two benchmark time series; we found that the proposed model is able to forecast the time series with more accuracy that other traditional approaches.