Journal of Economic Modeling Research

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As one of the important energy forms, natural gas consumption has an upward trend in recent years. Therefore management and planning for provision of it requires prediction of the future consumption. But many of prediction procedures are inherently stochastic therefore it is important to have better knowledge about the robustness of prediction procedures. This paper compares robustness of two prediction procedures Artificial Neural Networks as a nonlinear and ARIMA as a linear model. using resampling method to predict the monthly consumption of natural gas in the household sector. Data spans from 2001-4 to 2012-3, to train the networks, we used genetic algorithms and Particle Swarming Optimization then results were compared using 10-fold method. According to the results, the particle swarm optimization (PSO) outperforms the genetic algorithm. Then we used data from 2001-4 to 2010-3, with resampling by 2000 to predict the  natural gas consumption for the 2001 -4 to 2012-3 and to form critical values. Results show that prediction by a mixed method using ANN and PSO is more robust than ARIMA method.

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Forecasting tax revenues is vitally important issue for optimal allocation of taxable resources, planning and budgeting in national and regional levels and knowing the potential national participation in public expenditures.  The classical optimization based on mathematical methods may not be reliable in real world and mostly inefficient and inapplicable in complicated world due to their restricted assumptions. The smart optimization may help us to find the solution. This essay based on modified  PSO  methodology .The initial trial based on the data during 1971- 2007 in case of various direct and indirect taxes , and  using updated data  during 2008- 2012 for final forecasting , to estimate tax revenues for upcoming next three years (2013 up to 2016) by MATLAB software.