Abstract:
The main objective of this paper is to modeling and forecast exportsing of seafood in Iran. For this purpose, the method of collective self-explanatory time series moving average (ARIMA) and artificial neural networks are used. To perform the study, monthly data for the period of 1995:05 to 2008:02 estimated from model training period 2009:03 to 2011:02 data to verify the predictive power of the model. In this study, several criteria including Absolute Error (MAE), Mean Square Error (MSE), Average Percentage Error (MAPE), Root Mean Square Error (RMSE) and Root Mean Square Normalized Error (NRMSE) were used .The results show better performance of the neural network predicted non-linear statistical model ARIMA models and neural network structures studied neural networks Radial Basis Function (RBF) has the best performance in terms of error functions. Finally, for the two years 2012 and 2013 the amount of Iranian seafood export is predicted
Machine summary:
"net) Study of Generation of the Expenditure Profile of Young Men In Iran Mir Hossein Mousavi (PhD) and Batoul Azari Beni (MA) 1 Received: 2014/6/14 Accepted: 2015/5/5 Abstract: Knowledge about household expenditure profile in different ages of generations and investigating changes of their expenditures over time, have always been an important economic issue of household welfare during lifetime.
ir) Identifying Investment Priorities of Industry Sector in Line with Creating a Free Trade Zone in Bushehr Province Khadije Nasrollahi (PhD), Nematollah Akbari (PhD) and Atefe Ahmady (MA) 1 Received: 2014/10/6 Accepted: 2015/5/5 Abstract: Identifying the comparative advantage of economic activities in different areas, especially in the free trade zones, makes it possible for local and foreign investors to allocate resources optimally."