The Proposed Model For Prediction Of GDP Using With ARIMA, Neural Networks And Wavelet Transform

Subject Areas : Financial Knowledge of Securities Analysis

bita Shaygani ¹ , Amir behdad Salami ² , Ramin Khochiani ³

1 - Assistant Prof. In Economics Payame Noor University
2 - Phd In Economics Allameh Tabatabai
3 - Ph.D. Student In Economics, Payame Noor University

Received: 2014-04-04 Accepted : 2014-05-22 Published : 2014-12-22

Keywords: Forecasting GDP, Wavelet Transform, Neural Network,

Abstract :

Forecasting GDP, is one of the most important economic issues and due to its practical applications has attracted a lot of attentions. Methods of time series analysis and nonlinear methods such as neural network models as long as are used to forecast such variables . GDP's time series is variable that after the decomposition, with wavelet - a powerful tool for processing data- and analyzing the hidden layers, at some levels, has linear behavior and at other levels, has nonlinear behavior.Therefore, the proposed method would be thus that the time series of quarterly GDP for the period 1367 to 1389 using wavelet techniques are decomposed into different scale components. Next, the approximation level (trend) and cycles with linear behavior have predicted with ARIMA model, and cycles with the nonlinear behavior have predicted with neural network model.The results show that the performance of the proposed method is better than the neural network (NARNET) and ARIMA models.