TY  - RPRT
M1  - working_paper
AV  - none
KW  - Keywords: ARIMA
KW  -  Radial basis function
KW  -  Multilayer perceptron
KW  -  Generalized regression neural networks
KW  -  stationarity
KW  -  unit root  -  JEL Classification: C22
KW  -  C32
KW  -  C45
KW  -  C53
EP  - 18
UR  - http://ssrn.com/abstract=1368675
ID  - eprints1627
VL  - 10.2139/ssrn.1368675
N2  - This paper examines the estimation and forecasting performance of ARIMA models in comparison with some of the most popular and common models of neural networks. Specifically we provide the estimation results of AR-GRNN (Generalized regression neural networks) and the AR-RBF (Radial basis function). We show that neural networks models outperform the ARIMA forecasting. We found that the best model in the case of real US GNP is the AR-GRNN and for US unemployment rate is the AR-MLP. 
TI  - ARIMA and Neural Networks: An Application to the Real GNP Growth Rate and the Unemployment Rate of U.S.A.
A1  - Giovanis, Eleftherios
Y1  - 2009/03//
ER  -