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Agriculture Commodity Prices Forecasting Using a Fuzzy Inference System

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Agricultural Cooperative Management and Policy

Part of the book series: Cooperative Management ((COMA))

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Abstract

The objective of this chapter is to present a forecasting model of agricultural commodity prices using a Fuzzy Inference System. Recent studies have addressed the problem of commodity prices forecasting using different methods including artificial neural network and conventional model-based approaches. In this chapter, we proposed the use of a hybrid intelligent system called the Adaptive Neuro Fuzzy Inference System (ANFIS) to forecast agri-commodity prices. In ANFIS, both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic are combined in order to provide enhanced forecasting capabilities compared to using a single methodology alone. Point accuracy of four agri-commodity prices (wheat, sugar, coffee, and cocoa) is appraised by computing root-mean-squared forecast errors and other well-known error measures. In terms of forecasting performance, it is clear from the empirical evidence that the ANFIS model outperforms over a feedforward neural network and two other conventional models (AR and ARMA).

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References

  • Adam, E. G. (2003). Data-driven linguistic modeling using relational fuzzy rules. IEEE Transactions on Fuzzy Systems, 11(1), 121–134.

    Article  Google Scholar 

  • Atsalakis, G., & Valavanis, K. (2010). Forecasting stock trends using a combined technical analysis and a neuro-fuzzy based approach. Journal of Financial Decision Making, 6(1), 79–94.

    Google Scholar 

  • Atsalakis, G., & Valavanis, K. (2009). Forecasting stock Market short-term trends using a neuro-fuzzy based methodology. Journal of Expert Systems with Applications, 36(7), 10696–10707.

    Article  Google Scholar 

  • Atsalakis G. (2007). Wind energy production forecasting by neural networks and genetic algorithms. Athens: European Computer conference.

    Google Scholar 

  • Atsalakis, G., Skiadas, C., Braimis, I. (2007). Probability of trend prediction of exchange rate by neuro-fuzzy techniques. Recent advances in stochastic modeling and data analysis (pp. 414–422). London: World Scientific Publishing.

    Google Scholar 

  • Atsalakis, G., Minudaki, C. (2007). Prediction of Daily irrigation water demand using Adaptive Neuro-fuzzy inference system (ANFIS), International Conference on energy, environment, ecosystems and sustainable development (pp. 368–373). Ag. Nikolaos, Greece: Greece Water Economics, Statistics and Finance.

    Google Scholar 

  • Atsalakis, G., & Ucenic, C. (2006). Forecasting the production level for wind energy using a neuro-fuzzy model. Journal of WSEAS Transactions on Environment and Development, 2(6), 823–828.

    Google Scholar 

  • Atsalakis, G. (2005). Exchange rate forecasting by Neuro-Fuzzy Techniques. Journal of Financial Decision Making, I (2), 15–26.

    Google Scholar 

  • Azadeh, A., et al. (2012). A flexible neural network-fuzzy mathematical programming algorithm for improvement of oil price estimation and forecasting. Computers & Industrial Engineering, 62, 421–430.

    Article  Google Scholar 

  • Danthine, J.-P. (1977). Martingale, market efficiency and commodity prices. European Economic Review, 10, 1–17.

    Article  Google Scholar 

  • Deaton, A., & Laroque, G. (1996). Competitive storage and commodity price dynamics. Journal of Political Economy, 104, 896–923.

    Article  Google Scholar 

  • El Hédi, Arouri M., et al. (2012). Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models. Energy Economics, 34, 283–293.

    Article  Google Scholar 

  • Gustafson, R. L. (1958). Carry over levels for grains. Washington: US Department of Agriculture, Technical Buttletin. 1178.

    Google Scholar 

  • Hart, O. D., & Kreps, D. M. (1986). Price destabilizing speculation. Journal of Economy, 94, 927–952.

    Google Scholar 

  • He, K. et al. (2012). Crude oil price analysis and forecasting using wavelet decomposed ensemble model. Energy, 46, 564–574.

    Article  Google Scholar 

  • Hornik, K. (1991). Approximation capabilities of multi-layer feed-forward networks. Neural Networks, 4, 251–257.

    Article  Google Scholar 

  • Hu J. et al. (2012): Applying neural networks to prices prediction of crude oil futures. Hindawi Publishing Corporation Mathematical Problems in Engineering, 2012, Article ID 959040.

    Google Scholar 

  • Jain, B. A., & Nag, B. N. (1997). Performance evaluation of neural network decision models. Manage Information Systems, 14, 201–216.

    Google Scholar 

  • Jang, J. S. R. (1993). ANFIS: Adaptive network-based fuzzy inference system. IEEE Trans Systems, Man Cybern, 23(3), 665–685.

    Article  Google Scholar 

  • Jang, J. S. R., & Chuen-Tsai, S. (1995). Neuro-fuzzy modeling and control Proc. IEEE, 83, 378–406.

    Article  Google Scholar 

  • Jang, J-S.R., Sun, C-T.E., Mizutani, E. (1997). Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, New Jersy: Prentice Hall.

    Google Scholar 

  • Kohn, M. (1978). Competitive speculation. Econometrica, 46, 1061–1076.

    Article  Google Scholar 

  • Lee, C. C. (1990a). Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Transaction on System, Man Cybern, 20, 404–418.

    Article  Google Scholar 

  • Lee, C. C. (1990b). Fuzzy logic in control systems: fuzzy logic controller. II. IEEE Transaction on System, Man Cybern, 20, 419–435.

    Article  Google Scholar 

  • Loukas, Y. L. (2001). Adaptive neuro-fuzzy inference system: an instant and architecture-free predictor for improved QSAR studies. Journal of Medicinal Chemistry, 44, 2772–2783.

    Article  Google Scholar 

  • Makridakis, S., Weelwright, S. C., & McGee, V. E. (1983). Forecasting: methods and applications (2nd ed.). New York: Wiley.

    Google Scholar 

  • McNelis, D. P. (2005). Neural networks in finance: gaining predictive edge in the market (p. 21). USA: Elsevier Academic Press.

    Google Scholar 

  • Muth, J. F. (1961). Rational expectations and the theory of price movements. Econometrica, 29, 315–335.

    Article  Google Scholar 

  • Newbery, D. M. G., & Stiglitz, J. E. (1982). Optimal commodity stockpiling rules. Oxford Economic Papers, 34, 403–427.

    Google Scholar 

  • Skapura, D.(1996). Building neural networks (1), New York: Addison Wesley.

    Google Scholar 

  • Shapiro, A. F. (2002). The merging of neural networks, fuzzy logic, and genetic algorithms. Insurance: Mathematics and Economics, 31, 115–131.

    Google Scholar 

  • Pao, Y. H. (1989). Adaptive pattern recognition and neural networks. New York: Addison Wesley.

    Google Scholar 

  • Ross, T. J. (1997). Fuzzy logic with engineering Applications (International Edition). New York: McGraw-Hill.

    Google Scholar 

  • Salant, S. W. (1983). The vulnerability of price stabilization schemes to speculative attack. Journal of Political Economy, 91, 1–38.

    Article  Google Scholar 

  • Samuelson, P. A. (1971). Stochastic speculative price. Proceedings of the National Academy of Science, 68, 335–337.

    Article  Google Scholar 

  • Schechtman, J., & Escudero, V. (1977). Some results on An income fluctuation problem’. Journal of Economic Theory, 16, 151–166.

    Article  Google Scholar 

  • Scheinkman, J. A., & Schechtman, J. (1983). A simple competitive model with production and storage. Review of Economic Studies, 50, 427–441.

    Article  Google Scholar 

  • Ucenic, C., Atsalakis, G. (2006). Forecasting the electricity demand using a neuro-fuzzy approach versus traditional methods, Journal of WSEAS Transactions on Business and Economics, 3(1), pp.9–17.

    Google Scholar 

  • Wang L. X. (1992). Fuzzy systems are universal approximators, Proceedings of the 1st IEEE Conference Fuzzy Systems (Vol. 8, Issue 12, pp. 1163–1170), San Diego, CA.

    Google Scholar 

  • Williams, J. C., & Wright, B. D. (1991). Storage and commodity markets. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Wright, B. D., & Williams, J. C. (1982). The economic role of commodity storage. Economic Journal, 92, 596–614.

    Article  Google Scholar 

  • Xu, B. X., & Ouenniche, J. (2012). A data envelopment analysis-based framework for the relative performance evaluation of competing crude oil prices’ volatility forecasting models. Energy Economics, 34, 576–583.

    Article  Google Scholar 

  • Yu, W. (2012). Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models? Physica A, 391, 5546–5556.

    Article  Google Scholar 

  • Zhou, S., et al. (2012). A dynamic meta-learning rate-based model for gold market forecasting. Expert Systems with Applications, 39, 6168–6173.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Production and Management Engineering, Technical University of Crete, Crete, Greece

    George S. Atsalakis

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  1. George S. Atsalakis
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Correspondence to George S. Atsalakis .

Editor information

Editors and Affiliations

  1. Department of Production Engineering and Management, Technical University of Crete, Chania, Greece

    Constantin Zopounidis

  2. Department of Marketing, Maastricht University School of Business and Economics, Maastricht, The Netherlands

    Nikos Kalogeras

  3. School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Konstadinos Mattas

  4. Center for Entrepreneurship, Nyenrode Business University, Breukelen, The Netherlands

    Gert van Dijk

  5. Business Economics & Management, Mediterrean Agronomic Inst. of Chania, Chania, Greece

    George Baourakis

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Atsalakis, G.S. (2014). Agriculture Commodity Prices Forecasting Using a Fuzzy Inference System. In: Zopounidis, C., Kalogeras, N., Mattas, K., van Dijk, G., Baourakis, G. (eds) Agricultural Cooperative Management and Policy. Cooperative Management. Springer, Cham. https://doi.org/10.1007/978-3-319-06635-6_19

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