[1] S. Shafiee, ErkanTopal, An overview of global gold market and gold price forecasting, ResourcesPolicy, 35 (2010) 178-189.
[2] L. Yu, Visibility graph network analysis of gold price time series, Physica A: Statistical Mechanics and its Applications, 392(16) (2013) 3374–3384.
[3] S. Zhou, K.K. Lai, J. Yen, A dynamic meta-learning rate-based model for gold market forecasting, Expert Systems with Applications, 39 (2012) 6168–6173.
[4] S.F.M. Hussein, M.B.N. Shah, M.R.A. Jalal, S.S. Abdullah, Gold Price Prediction Using Radial Basis Function Neural Network, in: 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), IEEE, Kuala Lumpur, 2011, pp. 1 - 11.
[5] E. Hadavandi, A. Ghanbari, S. Abbasian-Naghneh, Developing a Time Series Model Based On Particle Swarm Optimization for Gold Price Forecasting, in: Third International Conference on Business Intelligence and Financial Engineering, IEEE, 2010.
[6] M. Ghiassia, H. Saidaneb, D.K. Zimbra, A dynamic artificial neural network model for forecasting time series events, International Journal of Forecasting, 21 (2005) 341-362.
[7] S. Mirmirani, H.C. Li, Gold Price, Neural Networks and Genetic Algorithm, Computational Economics, 23 (2004) 193–200.
[8] A.e.L.S. Maia, F.d.A.T.d. Carvalho, Holt’s exponential smoothing and neural network models for forecasting interval-valued time series, International Journal of Forecasting, 27 (2011) 740-759.
[9] T. Hida, Brownian motion, in: Brownian Motion, Springer, 1980, pp. 44-113.
[10] C. Park, W. Padgett, Accelerated degradation models for failure based on geometric Brownian motion and gamma processes, Lifetime Data Analysis, 11(4) (2005) 511-527.
[11] F.A. Postali, P. Picchetti, Geometric Brownian motion and structural breaks in oil prices: a quantitative analysis, Energy Economics, 28(4) (2006) 506-522.
[12] M.P. Taylor, D.A. Peel, L. Sarno, Nonlinear Mean. Reversion in Real Exchange Rates: Toward a Solution to the Purchasing Power Parity Puzzles, International economic review, 42(4) (2001) 1015-1042.
[13] J.M. Poterba, L.H. Summers, Mean reversion in stock prices: Evidence and implications, Journal of financial economics, 22(1) (1988) 27-59.
[14] A. Kian, A. Keyhani, Stochastic price modeling of electricity in deregulated energy markets, in: System Sciences, 2001. Proceedings of the 34th Annual Hawaii International Conference on, IEEE, 2001, pp. 7 pp.
[15] R.G. Haight, T.P. Holmes, Stochastic price models and optimal tree cutting: results for loblolly pine, (1991).
[16] C. Blanco, D. Soronow, Jump diffusion processes-energy price processes used for derivatives pricing and risk management, Commodities now September 2001a, 2 (2001) 83-87.
[17] J. Lee, J.A. List, M.C. Strazicich, Non-renewable resource prices: Deterministic or stochastic trends?, Journal of Environmental Economics and Management, 51(3) (2006) 354-370.
[18] S. Shafiee, E. Topal, Introducing a new model to forecast mineral commodity price, in: First International Future Mining Conference & Exhibition 2008, Australasian Institute of Mining and Metallurgy, 2008, pp. 243-250.
[19] M.A.G. Dias, K.M.C. Rocha, Petroleum concessions with extendible options using mean reversion with jumps to model oil prices, in: 3rd Real Options Conference, 1999, pp. 1-27.
[20] D.G. Laughton, H.D. Jacoby, Reversion, timing options, and long-term decision-making, Financial Management, (1993) 225-240.
[21] S. Kazemi, E. Hadavandi, F. Mehmanpazir, M.M. Nakhostin, A hybrid intelligent approach for modeling brand choice and constructing a market response simulator, Knowledge-Based Systems, 40 (2013) 101- 110.
[22] M. Aydinalp-Koksal, V.I. Ugursal, Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector, Applied Energy, 85(4) (2008) 271- 296.
[23] Y. Shimoda, Y. Yamaguchi, T. Okamura, A. Taniguchi, Y. Yamaguchi, Prediction of greenhouse gas reduction potential in Japanese residential sector by residential energy end-use model, Applied Energy, 87(6) (2010) 1944-1952.
[24] J.A. Rodger, A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings, Expert Systems with Applications, 41(4) (2014) 1813-1829.
[25] R. Hafezi, A. Akhavan, A NOVEL CONCEPTUAL RISK MANAGEMENT MODEL BASED ON THE FUTURE’S UNCERTAINTIES, in: 8th International Scientific Conference “Business and Management, Vilnius, LITHUANIA, 2014.
[26] M. Alipour, S. Alighaleh, R. Hafezi, M. Omranievardi, A new hybrid decision framework for prioritizing funding allocation to Iran’s energy sector, Energy, 121 (2017) 388-402.
[27] R. Hafezi, A. Akhavan, S. Pakseresht, Projecting plausible futures for Iranian oil and gas industries: Analyzing of historical strategies, Journal of Natural Gas Science and Engineering, 39 (2017) 15-27.
[28] M. Alipour, R. Hafezi, M. Amer, A. Akhavan, A new hybrid fuzzy cognitive map-based scenario planning approach for Iran’s oil production pathways in the postesanction period, Energy, 135 (2017) 851e864.
[29] C. Baumeister, L. Kilian, Real-time analysis of oil price risks using forecast scenarios, (2011).
[30] Ö. Dilaver, Z. Dilaver, L.C. Hunt, What drives natural gas consumption in Europe? Analysis and projections, Journal of Natural Gas Science and Engineering, 19 (2014) 125-136.
[31] A. Yazdani-Chamzini, S.H. Yakhchali, D. Volungevičienė, E.K. Zavadskas, Forecasting gold price changes by using adaptive network fuzzy inference system, Journal of Business Economics and Management, 13(5) (2012) 994-1010.
[32] C. Liu, To Integrate Text Mining and Artificial Neural Network to Forecast Gold Futures Price, in: International Conference on Management and Service Science IEEE, 2009, pp. 1- 4
[33] S. Zhou, K.K. Lai, An Improved EMD Online Learning- Based Model for Gold Market Forecasting, Intelligent Decision Technologies, 10 (2011) 75-84.
[34] Wensheng Dai, Chi-Jie Lu, T. Chang, Empirical Research of Price Discovery for Gold Futures Based on Compound Model Combing Wavelet Frame with Support Vector Regression, Artificial Intelligence and Computational Intelligence, 6320 (2010) 374-381.
[35] F. Zhang, Z. Liao, Gold Price Forecasting Based on RBF Neural Network and Hybrid Fuzzy Clustering Algorithm, in: J. Xu, J.A. Fry, B. Lev, A. Hajiyev (Eds.) Proceedings of the Seventh International Conference on Management Science and Engineering Management, Springer Berlin Heidelberg, 2014, pp. 73-84.
[36] J. Kumar, T. Rao, S. Srivastava, Economics of Gold Price Movement-Forecasting Analysis Using Macro-economic, Investor Fear and Investor Behavior Features, in: S. Srinivasa, V. Bhatnagar (Eds.) Big Data Analytics, Springer Berlin Heidelberg, 2012, pp. 111-121.
[37] C. Pierdzioch, M. Risse, S. Rohloff, A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss, Resources Policy, 47 (2016) 95-107.
[38] K. Gangopadhyay, A. Jangir, R. Sensarma, Forecasting the price of gold: An error correction approach, IIMB Management Review, 28(1) (2016) 6-12.
[39] L. Xian, K. He, K.K. Lai, Gold price analysis based on ensemble empirical model decomposition and independent component analysis, Physica A: Statistical Mechanics and its Applications, 454 (2016) 11-23.
[40] W. Kristjanpoller, M.C. Minutolo, Gold price volatility: A forecasting approach using the Artificial Neural Network–GARCH model, Expert Systems with Applications, 42(20) (2015) 7245-7251.
[41] D.G. Baur, J. Beckmann, R. Czudaj, A melting pot— Gold price forecasts under model and parameter uncertainty, International Review of Financial Analysis, 48 (2016) 282-291.
[42] H. Dehghani, M. Ataee-pour, Determination of the effect of operating cost uncertainty on mining project evaluation, Resources Policy, 37(1) (2012) 109-117.
[43] H. Dehghani, M. Ataee-pour, A. Esfahanipour, Evaluation of the mining projects under economic uncertainties using multidimensional binomial tree, Resources Policy, 39 (2014) 124-133.
[44] T. Kriechbaumer, A. Angus, D. Parsons, M.R. Casado, An improved wavelet–ARIMA approach for forecasting metal prices, Resources Policy, 39 (2014) 32-41.
[45] Y. Chen, K. He, C. Zhang, A novel grey wave forecasting method for predicting metal prices, Resources Policy, 49 (2016) 323-331.
[46] Y. Chen, Y. Zou, Y. Zhou, C. Zhang, Multi-step-ahead Crude Oil Price Forecasting based on Grey Wave Forecasting Method, Procedia Computer Science, 91 (2016) 1050-1056.
[47] D. Liu, Z. Li, Gold Price Forecasting and Related Influence Factors Analysis Based on Random Forest, in: Proceedings of the Tenth International Conference on Management Science and Engineering Management, Springer, 2017, pp. 711-723.
[48] C. Liu, Z. Hu, Y. Li, S. Liu, Forecasting copper prices by decision tree learning, Resources Policy, 52 (2017) 427-434.
[49] K.C. Sivalingam, S. Mahendran, S. Natarajan, Forecasting gold prices based on extreme learning machine, International Journal of Computers Communications & Control, 11(3) (2016) 372-380.
[50] B. Guha, G. Bandyopadhyay, Gold Price Forecasting Using ARIMA Model, Journal of Advanced Management Science Vol, 4(2) (2016).
[51] R.K. Sharma, Forecasting Gold price with Box Jenkins Autoregressive Integrated Moving Average Method, Journal of International Economics, 7(1) (2016) 32.
[52] X.S. Yang, A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer Berlin Heidelberg, 2010, pp. 65–74.
[53] N.S. Jaddi, S. Abdullah, A.R. Hamdan, Optimization of neural network model using modified bat-inspired algorithm, Applied Soft Computing, 37 (2015) 71-86.
[54] R. Hafezi, J. Shahrabi, E. Hadavandi, A bat-neural network multi-agent system (BNNMAS) for stock priceprediction: Case study of DAX stock price, Applied Soft Computing, 29 (2015) 196–210.
[55] H. Dehghani, D. Bogdanovic, Copper price estimation using bat algorithm, Resources Policy, in press (2017).
[56] R. Svečko, D. Kusić, Feedforward neural network position control of a piezoelectric actuator based on a BAT search algorithm, Expert Systems with Applications, 42(13) (2015) 5416-5423.
[57] G. Zhang, B.E. Patuwo, M.Y. Hu, Forecasting with artificial neural networks:The state of the art, International Journal of Forecasting, 14(1) (1998) 35–62.
[58] L.N. Trefethen, Spectral methods in MATLAB, SIAM, 2000.
[59] P. Box, G.M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-day Inc, San Francisco, CA, 1976.
[60] G.S. Atsalakis, E.M. Dimitrakakis, C.D. Zopounidis, Elliott Wave Theory and neuro-fuzzysystems, in stock market prediction: the WASP system,, Expert Systems with Applications, 38 (2011) 9196–9206.
[61] A.H. Fath, Application of radial basis function neural networks in bubble point oil formation volume factor prediction for petroleum systems, Fluid Phase Equilibria, (2017).
[62] R.J. Schalkoff, Artificial neural networks, McGraw-Hill Higher Education, 1997.
[63] D.F. Specht, A general regression neural network, IEEE transactions on neural networks, 2(6) (1991) 568-576.
[64] R. Hu, S. Wen, Z. Zeng, T. Huang, A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm, Neurocomputing, 221 (2017) 24-31.
[65] I.A. Gheyas, L.S. Smith, Feature subset selection in large dimensionality domains, Pattern recognition, 43(1) (2010) 5-13.
[66] J. Park, K.-Y. Kim, Meta-modeling using generalized regression neural network and particle swarm optimization, Applied Soft Computing, 51 (2017) 354- 369.
[67] A. Moghadassi, F. Parvizian, S. Hosseini, A new approach based on artificial neural networks for prediction of high pressure vapor-liquid equilibrium, Australian Journal of Basic and Applied Sciences, 3(3) (2009) 1851-1862.
[68] E. Heidari, M.A. Sobati, S. Movahedirad, Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN), Chemometrics and Intelligent Laboratory Systems, 155 (2016) 73-85.