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This study investigates predictability, chaos analysis, wavelet decomposition and the performance of neural network models in forecasting the return series of the Tehran Stock Exchange Index (TEDPIX). For this purpose, the daily data from April 24, 2009 to May 3, 2012 is used. Results show that TEDPIX series is chaotic and predictable with nonlinear effect. Also, according to obtained inverse of the largest lyapunov exponent, we are able to predict the future values of the series up to 31 days. Besides, our findings suggest that multi-layer feed forward neural network model and fuzzy model based on decomposed data, are of superior performances in predicting the return series. It is worth mentioning that, among these models, MFNN reveals the best performance.

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Public pricing of products is one of the most important economicalissues, since any changes in the pricing, affects both the welfare ofconsumers and quantity of goods and Services which are produced.
In this paper which is done for natural gas pricing  in Iran, the purpose is giving a price that the government can consider it as a suitable choice for using in subsidies targeting project. These prices have two advantages: first, they try to maximum the social economical welfare (summation of producer and consumer surplus) second, this method solve the problem that the producer has in covering their costs (by marginal cost pricing) because of increasing returns to scale.
This paper deals with the optimal gas pricing in household sector in Iran by the Ramsey method of pricing.
In this regard we have used fuzzy regression (because of its accuracy and devoid of classic regression restrictions) and the data from 1977 to 2011 for estimating production function and returns to scale in natural gas production side. Also for estimating demand function and elasticity we have used ARDL method and data from 1350 to 1389. The results shows that the current prices aren’t optimum and despite implementation of subsidies targeting project the prices are low.