Sajadi, H., Farazmand, H., & Alisufi, H. (2010). Investigating the relationship between macroeconomic variables and stock index returns in cash: Tehran Stock Exchange. Journal of macroeconomics (journal of economics sciences) 10(2), 123-150.
 Mehrara, M., Certain, A., Ahrari, M., & Hamouni, A. (2013). Modeling and predicting the Tehran Stock Exchange Index and determining the effective variables on it. Quarterly journal of economic research and policy, 50.
 Golestani, S., Ansari.L., S, & Abbaspour, R. (2014). Oil price forecast with ARFIMA_GARCH and fuzzy logic. The journal of energy economics studies, 10(41), 153-174. (In Persian)
 Natarajan, G. S., & Ashok, A. (2018). Multivariate Forecasting of Crude Oil Spot Prices using Neural Networks. arXiv preprint arXiv:1811.08963.
 Liu, H., & Chang, Y. (2017). Research on international crude oil price forecasting model. International journal of new development engineering and society, 1(3), 78-81.
 Bategeka, L, N., and Matovu, J. M (2011). Oil wealth and potential dutch disease effects in Uganda. Economic policy research centre, 1-36
 Luo, Z., Cai, X., Tanaka, K., Takiguchi, T., Kinkyo, T., & Hamori, S. (2019). Can we forecast daily oil futures prices? Experimental evidence from convolutional neural networks. Journal of risk and financial management, 12(1), 9.
 Karimzadeh, S. D., & Honarvar, N. (2017). Investigating the long-run relationship between crude oil price, gold price, housing price index and exchange rate in Iran using a structural vector error correction approach. Journal of energy economics studies, 53, 135-164. (In Persian)
 Verharami, V., & Sadeghi, M. (2017). The asymmetric effect of crude oil prices on demand in selected OPEC countries is the price analysis and dynamic panel. Journal of energy economics studies, thirteenth, 52, 35-59. (In Persian)
 rezazadeh, a., & Jahangiri, K. (2017). Impact of oil price volatility on the economic growth of major oil-producing countries: vector autogeneration approach in panel Data (PVAR). Journal of energy economics studies, thirteenth, 52, 153-180. (In Persian)
 Dindar Rostami, M., Shirinbakhsh, S., & Afshari, Z. (2019). The effects of oil price shocks on discretionary fiscal policy in selected opec countries: panel structural vector autoregressive. Iranian journal of economic studies, 8(1), 7-25.
 Davies, P. (2007). What’s the Value of an Energy Economist?. Speech presented at the International Association for Energy Economics, Wellington, New Zealand.
 Baumeister, C., & Kilian, L. (2012). Real-time forecasts of the real price of oil. Journal of business & economic statistics, 30(2), 326-336.
 Baumeister, C., & Kilian, L. (2014). What central bankers need to know about forecasting oil prices? International economic review, 55(3), 869-889.
 Baumeister, C., Kilian, L and Zhou, X. (2014). Is product spreads useful for forecasting oil prices? an empirical evaluation of the verleger hypothesis, forthcoming, macroeconomic dynamics. Retrieved 20 September, 2019 from https://pdfs.semanticscholar.org/3b9d/1f71973aad8f9724648f214f0625713e07db.pdf
 Besso, C. R., & Pamen, E. P. F. (2017). Oil price shock and economic growth. Experience of cemac countries, 8(1).
 Aimer, N. M. M. (2016). The effects of fluctuations of oil price on economic growth of Libya. Energy economics letters, 3(2), 17-29.
 Ftiti, Z., Guesmi, K., Teulon, F., & Chouachi, S. (2016). Relationship between crude oil prices and economic growth in selected OPEC countries. Journal of applied business research, 32(1), 11.
 Nicholis, S. C., & Sumpter, D. J. T. (2011). A dynamical approach to stock market fluctuation. International journal of bifurcation and chaos, 21(12), 3557-564.
 Olaniyi, A. A., Adedotun, K. O., & Samuel, O. A. (2018). Forecasting methods for domestic air passenger demand in Nigeria. Journal of applied research on industrial engineering, 5(2), 146-155.
 Lee, Y. H., Hu, H. N., & Chiou, J. S. (2010). Jump dynamics with structural breaks for crude oil prices. Energy economics, 32(2), 343-350.
 Li, T., Hu, Z., Jia, Y., Wu, J., & Zhou, Y. (2018). Forecasting crude oil prices using ensemble empirical mode decomposition and sparse Bayesian learning. Energies, 11(7), 1882.
 Rasoli, S., Tabesh, H., & Etminani, K. (2018). Evaluation of artificial intelligence models of time series in forecasting the number of hospital inpatient admission. Journal of health and biomedical informations, 5(1), 12-24. (In Persian)
Mohaghegh, S., Richardson, M., & Ameri, S. (2001). Use of intelligent systems in reservoir characterization via synthetic magnetic resonance logs. Journal of petroleum science and engineering, 29(3-4), 189-204.
 Preeti, G., & Santi, B (2012). Stock market forecasting techniques: A survey. Journal of theoretical and applied information technology. 1(46). a24-30
 Weiss, W. W., Balch, R. S., & Stubbs, B. A. (2002, January). How artificial intelligence methods can forecast oil production. In SPE/DOE improved oil recovery symposium. Society of Petroleum Engineers.
 Keerthan, J, S. Nagasi, Y. & Shaik, S. (2019). Machine learning algorithms for oil in price prediction. International journal of innovative technology and exploring engineering (IJITEE), 8(8), 958.963.
 Dourra, H., & Siy, P. (2002). Investment using technical analysis and fuzzy logic. Fuzzy sets and systems, 127(2), 221-240.
 Shirouehzad, H., & Anvari, S. M. (2014). Prioritization of sustainable production indicators using fuzzy inference system. Journal of applied research on industrial engineering (JARIE), 1(2), 96-111.
 Washington. D. C. (2017). What Drives Crude Oil Prices. Retrieved 20 September, 2019 from https://knoema.com/WHTDRCOP2019Oct/what-drives-crude-oil-prices-october-2019
 Hammond, J. L. (2011). The resource curse and oil revenues in Angola and Venezuela. Science & society, 75(3), 348-378.
 Mehrara, M., & Makini, N, M. (2009). Investigation of the nonlinear relationship between oil revenues and economic growth using the limit method (Case of Iran). Journal of energy economics, 6(22), 29-52. (In Persian)
 Chien, C. (1990). Fuzzy logic in control systems: fuzzy logic controller. IEEE Trans Syst Man Cybern Part II, 20, 429-434.
 Kerre, E. E. (1992, August). A comparative study of the behavior of some popular fuzzy implication operators on the generalized modus ponens. In Fuzzy logic for the management of uncertainty (pp. 281-295). John Wiley & Sons, Inc.
 Cao, Z., & Kandel, A. (1989). Applicability of some fuzzy implication operators. Fuzzy sets and systems, 31(2), 151-186.