Designing The Most Suitable Hybrid Model for Forecasting The Future Price of Saffron in The Agricultural Commodity Exchange

Document Type : Research Paper

Authors

1 Researcher, Agricultural Planning, Economics and Rural Development Research Institute (APERDRI), Tehran, Iran

2 Research Assistant Professor, Agricultural Planning, Economics and Rural Development Research Institute (APERDRI), Tehran, Iran

Abstract

Saffron, as the most expensive agricultural and medicinal product in the world, has a special place in buying and selling related to the Iranian agricultural commodity bourse. The saffron commodity bourse, like any other market, has always faced limitations and structural problems, and most of these problems are related to its price fluctuations. Price forecasting using appropriate models can be a great help in reducing the price risk of futures market of saffron. The main question is that in in confronting with various price forecasting methods, which methods should be chosen to forecast the future price of saffron? The purpose of this study is to design the most appropriate hybrid model for forecasting the future price of Negin saffron in the agricultural commodity bourse, which consists of a set of nonlinear models of genetic algorithm, deep neural network, random forest, support vector machine and Monte Carlo method. In this hybrid model, genetic algorithm is used to determine the optimal lag of price time series, deep neural network, random forest model and support vector machine are used to forecast the price time series, and Monte Carlo method is used to simulate the most probable price probability. The results of this study showed that the forecasting accuracy of the hybrid model of "Genetic Algorithm-Deep Neural Network-Monte Carlo" is higher than the two models of "Genetic Algorithm-Random Forest-Monte Carlo" and "Genetic Algorithm-Support Vector Machine -Monte Carlo". Therefore, using a deep neural network and calculating the most probable price probability by the Monte Carlo method, provide the most accurate saffron price prediction with a high degree of reliability and minimal risk. Thus, it is suggested that that the management of the commodity bourse, stock market participants and researchers can use the advantages of this proposed model in forecasting the price of agricultural products.

Keywords

Main Subjects

References

  1. Borimnejad, V. & Bakeshloo, M. (2013). Forecasting the Price of Tomatoes: Comparison of Syncretistic Methods of Neural Network Auto-Regressive and ARIMA. Iranian Journal of Agricultural Economics and Development, 3(83): 89-103. (In Farsi)
  2. Das, S.P. & Padhy, S. (2015). A novel hybrid model using teaching learning-based optimization and a support vector machine for commodity future index forecasting. International Journal learning machine & Cyber, Springer. DOI: 10.1007/s13042-015-0359-0
  3. Duda, R.O., Hart, P.E. & Stork, D.G. (2011). Pattern classification. 2nd Edition. John Wiley & Sons, New York.
  4. (2019). World food and agriculture statistical pocketbook. Food and Agriculture Organization of the United Nations.
  5. Ghaderzadeh, H., Ahmadzadeh, Kh.  & Ganji, S. (2019). Determine the appropriate model to predict the price of Agricultural crops “A case of wheat, Alfa- Alfa and Potato crops. Journal of Agricultural Economics Research, 11(3), 23-40. (In Farsi)
  6. Ghahremanzadeh, M. & Rashid Ghalam, M. (2015). Seasonal forecasting of meat prices in Iran: Application of periodic autoregressive model. Iranian Journal of Agricultural Economics and Development Research, 46(3), 469-480. (In Farsi)
  7. Ghali-Zinoubi, Z. & Toukabri, M. (2019). The antecedents of the consumer purchase intention: sensitivity to price and involvement in organic product: moderating role of product regional identity. Journal of Trends in Food Science and Technology, 90, 175-179.
  8. Ghiass, M. (2014). An Introduction to the Monte Carlo Simulation Methods. Iranian Journal of Polymerization, 4(1): 67-77. (In Farsi)
  9. Guo, L., Chehata, N., Mallet, C. & Boukir, S. (2011). Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests. Journal of Photogram Remote Sensing, 66(1), 56-66.
  10. Gurung, B., Singh, K.N., Paul, R.K., Panwar, S., Gurung, B. and Lepcha, L. (2017). An alternative method for forecasting price volatility by combining models. Journal of Communications in Statistics-Simulation and Computation, 46(6), 4627-4636.
  11. Hasan, M. M., Zahara, M. T., Sykot, M. M., Hafiz, R., & Saifuzzaman, M. (2020). Solving onion market instability by forecasting onion price using machine learning approach. 2020 International Conference on Computational Performance Evaluation (ComPE), 777–780.
  12. Hoseini, S.M., Mazandarani zadeh, H. & Nazari, B. (2021). Simultaneously management of surface and groundwater resources and increasing farmers' resilience to water scarcity by predicting the price of agricultural products and using GA (case study of irrigation and drainage network of Qazvin plain). Iranian Journal of Soil and Water Research, 52(2), 563-576. (In Farsi)
  13. Khosravi, M., Shams, A., Gholizadeh, H. & Hoshmandan Moghadam, Z. (2017). Investigating factors related to selling behavior of Saffron farmers in the Qaen Township. Journal of Saffron Agronomy and Technology, 5(1), 91-105 (In Farsi)
  14. Kyriazi, F., Thomakos, D. D., & Guerard, J. B. (2019). Adaptive learning forecasting, with applications in forecasting agricultural prices. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2019.03.031
  15. Liu, H. (2016). A domestic soybean Price forecasting model based on improved Quantile-RBF neural network, thesis, Nanjing agricultural university, Nanjing.
  16. Lu, L. (2018). Optimal γ and C for є-support vector regression with RBF Kernels. https://arxiv.org/pdf/1506.03942.pdf
  17. Luo, G.Q. and Zhou, L. (2016). Analysis on the trend of agricultural product price fluctuation under the new economic normal. Journal of Statistics and Decision, 32(21), 83-86.
  18. Mahida, S., & Patel, B. (2018). A review of the application of data mining techniques for vegetable price prediction, https://1library.net/document/
  19. Mahmoudi, K., Ketabdari, M.J. & Ghasemi, H. (2018). Hybrid wavelet-SVM method to predict the occurrence of abnormal waves. Journal of Hydraulilics, 13-1(131), 1-15. (In Farsi)
  20. Moghadasi, R. & Jaleh Rajabi, M. (2013). Comparison of combined and conventional models in forecasting prices of wheat, corn and sugar. Journal of Agricultural Economics Research, 5(2), 1-22. (In Farsi)
  21. Murugesan, R., Mishraa, E. & Krishnan, A.H. (2021). Deep learning based models: Basic LSTM, Bi LSTM, Stacked LSTM, CNN LSTM and Conv LSTM to forecast Agricultural commodities prices. Research square: https://doi.org/10.21203/rs.3.rs-740568/v1.
  22. Peng, C. (2014). Research on improving the formation mechanism of vegetable prices. Journal of Review of Economic Research, 62, 45-50.
  23. Profeta, A. and Hamm, U. (2019). Who cares about local feed in local food products? Results from a consumer survey in Germany. British Food Journal, 121(3), 711-724.
  24. Raikar, R.V., Wang, Ch-Yi., Shih, H.P., Hong, J.H. (2016). Prediction of contraction scour using ANN and GA. Flow Measurement and Instrumentation, 50, 26-34.
  25. Rasheed, A., Younis, M.S., Ahmad, F., Qadir, J. & Kashif, M. (2021). District wise price forecasting of wheat in Pakistan using deep learning. 04781
  26. Sangwana, K.S., Saxenaa, S. & Kanta, G. (2016). Optimization of machining parameters to minimize surface roughness using integrated ANN-GA approach. The 22nd CIRP conference on Life Cycle Engineering, Procedia CIRP 29, 305-310.
  27. Schmidhuber, J., Pound, J., & Qiao, B. (2020). COVID-19: Channels of transmission to food and agriculture. FOA. https://doi.org/https://doi.org/10.4060/ca8430en
  28. Wang, L., Feng, J., Sui, X., Chu, X. & Mu, W. (2020). Agricultural product price forecasting methods: research advances and trend. British Food Journal, 122(7), 2121-2138.
  29. Wu, H., Zhu, M., Chen, W. & Chen, W. (2017). A new method of large-scale short-term forecasting of agricultural commodity prices: illustrated by the case of agricultural markets in Beijing, Journal of Big Data, 4, 1.
  30. Xiong, T., Li, Ch., Bao, Y., Hu, Zh. & Zhang, L. (2015). A combination method for interval forecasting of agricultural commodity futures prices, Knowledge-Based Systems 77, 92-102.
  31. Yang, X. (2007). A modified particle swarm optimizer with dynamic adaptation Baoding: applied mathematics and Computation 51, June-2007, Elsevier, 189, 1205-1213.
  32. Yang, Y., Chen, Y., Wang, Y., Li, C. & Li, L. (2016). Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecasting. Journal of Applied Soft Computing, 49: 663-675.
  33. Yeh, C., Chi, D.J. & Lin, Y.R. (2014). Going-concern prediction using hybrid random forests and rough set approach. Journal of Information Sciences, 254, 98-110.
  34. Yoon, H., Jun, S.C., Hyun, Y., Bae, G.O. & Lee, K.K. (2011). A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, Journal of Hydrology, 396, 128-138.
  35. Zare Mehrjerdi, M.R., Mehrabi Boshrabadi, H., Nezamabadi-pour, H. & Tohidi, A. (2015). Evaluation of artificial neural network-panel data hybrid model in predicting Iran’s dried fruits export prices. Quarterly Journal of Economics Quarterly, 12(3), 95-116. (In Farsi)
  36. Zhang, D., Zang, G., Li, J., Ma, K., & Liu, H. (2018). Prediction of soybean price in China using QR-RBF neural network model. Journal of Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2018.08.016
  37. Zolfaghari, M., Sahabi, B. & Bakhtiari, M.J. (2020). Designing a model to predict the returns of the total stock market index (with an emphasis on the combined models of deep learning network and GARCH family models). Iranian Journal of Financial Engineering and Portfolio Management, 11(42): 138-171. (In Farsi)
Iranian Journal of Agricultural Economics and Development Research
Volume 53, Issue 4
November 2022
Pages 1023-1041

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