Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks

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J D Velásquez
C J Franco


prediction, nonlinear macroeconomics, SARIMA, multilayer perceptrons.


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 theproposed model is able to forecast the time series with more accuracy that other traditional approaches.

MSC: 62M10, 62M20


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