References
Abbasi, GH. R. (2009). Convergence between Financial Development and Industrial Production in Iran. Economical Modeling, 3(7), 137-154.
Acedański, J. (2013). Forecasting industrial production in Poland – A comparison of different methods. Ekonometria Econometrics, 1(39), 40-51.
Assaf, N. A. (2011). Mercado financeiro. (10th ed.). So Paulo: Editora Atlas.
Atsalakis, G. S., Dimitrakakis E. M., and C. D. Zopounidis (2011). Elliot Wave Theory and neuro-fuzzy systems, stock market prediction: The WASP system. Expert Systems with Applications, 38, 9196– 9206.
Ayodele, A. A., Aderemi, O. A. and Charles, K. A. (2014). Stock Price Prediction Using the ARIMA Model, School of Mathematic, Statistics & Computer Science, 16th International Conference on Computer Modelling and Simulation, 105-111.
Azar, A. and Karimi, S. (2010). Neural Network Forecasts of Stock Return Using Accounting Ratios. Financial Research Journal, 11(28), 3-20.
Bakhtiari, S. and Salem, B. (2009). The effects of trade liberalization on the trade of products under the industrial sectors of Iran. Economics Research, 8(31), 15-27.
Central Bank website, statistics and data (
www.cbi.ir).
Chen, M. Y. and Chen, B. T. (2015). A hybrid fuzzy time series model based on granular computing for stock price forecasting. Information Sciences, 294, 227-241.
Chester, M. (1993). Neural networks: a tutorial. Prentice-Hall, Inc.
Fahim Yahyaee, F. and Falihi, N. (2003). The Impact of Monetary and Financial Policies on the Industry in the Last 25 Years. Economic Research, 3(8), 199-215.
Günay, M. (2018). Forecasting industrial production and inflation in Turkey with factor models. Central Bank Review, 18(4), 149-161.
Hadinejad, M. and Mehrabian, A. (2008). AN Examination of Credits Bank Facilities Effects on Irans Manufacturing Industry Growth. Journal of Management System (Financial Economics and Development), 1(2), 75 – 85.
Heravi, S., Osborn, D. R., and Birchenhall, C. R. (2004). Linear versus neural network forecasts for European industrial production series. International Journal of Forecasting, 20(3), 435-446.
Hykin, S. (1994). Neural networks: A comprehensive foundation. New York: Macmillan.
Hykin, S. (1999). Neural Networks: A Comprehensive Foundation. Printice- Hall, Inc., New Jersey.
Jang, J. Sh. R. (1993). ANFIS: Adap tive-Ne twork-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665- 685.
Kangarani Farahani, M. and Mehralian, S. (2013). Comparison between Artificial Neural Network and Neuro-Fuzzy for Gold Price Prediction. 13th Iranian Conference on Fuzzy Systems (IFSC).
Kassem, Y., Çamur, H., and Esenel, E. (2017). ANFIS and Response Surface Methodology (RSM) prediction of biodiesel dynamic viscosity at 313 K. 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception.
Kavitha Mayilvaganan, M. and Naidu, K. B. (2011). Comparative Study of ANN and ANFIS for the Prediction of Groundwater Level of a Watershed. Global Journal of Mathematical Sciences: Theory and Practical, 3(4), 299-306.
Mirsoltani, S. M. and Niaki, S. T. A. (2013). Forecasting Energy Price and Consumption for Iranian Industrial Sectors Using ANN and ANFIS. Iranian Journal of Economic Studies, 2(1), 73-102.
Mohamad Alizadeh, A., Raei, R. and Mohammadi, Sh. (2015). Prediction of stock market crash using self-organizing maps. Financial Research Journal, 17(1), 159-178.
Monjazeb, M. R. (2000). Evaluation of the Effect of Liquidity on the Value Added of the Industrial Sector. Journal of Economic Research and Policies, 7(12), 39-59.
Murat, Y. S. and Ceylan, H. (2006). Use of artificial neural networks for transport energy demand modeling. Energy Policy, 34(17), 3165-3172.
Pablo-Romero, M. P., and Sanchez-Braza, A., (2015). Productive energy use and economic growth: energy, physical and human capital relationships. Energy Econ. 49, 420-429.
Pablo-Romero, M. P., Sanchez-Braza, A. and Exposito, A. (2019). Industry level production functions and energy use in 12 EU countries.
Raghuramg, R. and Luigi, Z. (2002).Financial system industrial structure and growth. Oxford review of Economic Policy, 17(4).
Samouel, B. and Aram, B. (2016). The Determinants of Industrialization: Empirical Evidence for Africa. European Scientific Journal, 219-239.
Samsami, H. and
Amirjan, R. (2011). The Effect of Banking Facilities on the Value-Added of the Industry and Mining Sector in Iran.
Journal of Economic Research and Policies. 19(59), 129- 150.
Samsami, H., Davoodi, P. and Amiri Javid, H. (2016). Comparing Effectiveness of Liquidity Growth on GDP, Private Investment and Employment with Assets Market Bubble, Journal of Economic Research, 51(2), 457-493.
Sarmad, A. A. (2017). A Comparative Study of Artificial Neural Networks and Adaptive Nero- Fuzzy Inference System for Forecasting Daily Discharge of a Tigris River. International Journal of Applied Engineering Research, 12(9), 2006-2016.
Shahjahan, A., Khandaker, J. A. and Shafiul Islam, Md. (2016). Effects of Trade Openness and Industrial Value Added on Economic Growth in Bangladesh. International Journal of Sustainable Development Research, 2(3), 18-23.
Sözen, A. and Arcaklioğlu, E. (2007). Prospects for future projections of the basic energy sources in Turkey. Energy Sources, Part B, 2(2), 183-201.
Tiwari, M. K., Bajpai, S. and Dewangan, U. K. (2012). Prediction of Industrial Solid Waste with ANFIS Model and its comparison with ANN Model- A Case Study of Durg-Bhilai Twin City India. International Journal of Engineering and Innovative Technology, 2(6), 192-201.
Van Eyden, R. J. (1996). The Application of Neural Networks in the Forecasting of Share Prices. Finance and Technology Publishing, Haymarket, VA.
Wacziarg, R. (2000). Measuring the Dynamic Gains from Trade. The World Bank Economic Review, 15(34), 55-78.
Zamanzadeh, H. (2010). A decade of Iran's economy performance in terms of macroeconomic indicators. Tazehaye Eghtesad, 8(129), 35-43.
Zarra Nejad, M. and Raoofi, A. (2015). Evaluation and Comparison of Forecast Performance of Linear and Non-linear Methods for Daily Returns of Tehran Stock Exchange. Financial Monetary Economics Research, 22(9), 1-28.