Industry is one of the important and fundamental parts of economic and a ground for economic growth and development. Development and growth in industry section provide the ground for growth and development of other section such as agricultural, service, transport and energy. This section plays an important role in providing job in development process. According to the importance of forecasting in economic planning and policy-making and the importance of employment in industrial section, the present study dealt with forecasted number of industrial employment of Iran ANN and ARIMA artificial neural network method. For this reason 1358-1390 data were used. examine the validity of the research Mean Absolute Percentage Error MAPE, Root Mean Square Error RMSE and Theil U Ststistic were used. The research results show that back-propagation neural network has a high power in forecasting industrial employment in Iran and has lesser error in comparison to ARIMA method.  Â
Jafari Samimi, A., & dehghan, Z. (2014). Forecasting Industrial Employment in Iran using Artifical Neural Network Method and ARIMA Model. Journal of Econometric Modelling, 1(1), 33-49. doi: 10.22075/jem.2017.1496
MLA
Ahmad Jafari Samimi; Zahra dehghan. "Forecasting Industrial Employment in Iran using Artifical Neural Network Method and ARIMA Model", Journal of Econometric Modelling, 1, 1, 2014, 33-49. doi: 10.22075/jem.2017.1496
HARVARD
Jafari Samimi, A., dehghan, Z. (2014). 'Forecasting Industrial Employment in Iran using Artifical Neural Network Method and ARIMA Model', Journal of Econometric Modelling, 1(1), pp. 33-49. doi: 10.22075/jem.2017.1496
VANCOUVER
Jafari Samimi, A., dehghan, Z. Forecasting Industrial Employment in Iran using Artifical Neural Network Method and ARIMA Model. Journal of Econometric Modelling, 2014; 1(1): 33-49. doi: 10.22075/jem.2017.1496
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