ارائه یک مدل ترکیبی انعطاف‌پذیر برای پیش‌بینی قیمت محصولات کشاورزی؛ مطالعه موردی بازار گوشت ایران

نوع مقاله : مقالات پژوهشی به زبان انگلیسی

نویسنده

موسسه پژوهش های برنامه ریزی، اقتصاد کشاورزی و توسعه روستایی

چکیده

موضوع قیمت یک عامل کلیدی در فعالیت مالی و تجاری مرتبط با بخش کشاورزی است، به گونه‌ای که همواره فعالان بخش کشاورزی در معرض ریسک‌های ناشی از نوسان قیمت محصولات کشاورزی قرار دارند. این مسئله نه تنها منجر به تصمیم‌گیری نادرست در زمینه تولید بهینه محصولات در سال جاری می‌شود، بلکه می‌تواند اجرای تعهدهای مالی آنان را در سال‌های آتی با خطر روبه‌رو سازد. در سال‌های اخیر، نوسانات قیمت محصولات کشاورزی در ایران افزایش یافته است و لذا پیش‌بینی دقیق تغییرات قیمت ضروری به نظر می‌رسد. در مطالعه حاضر، یک رویکرد ترکیبی انعطاف‌پذیر در پیش‌بینی قیمت ماهیانه گوشت گاو، گوشت گوسفند و مرغ از آوریل 2001 تا مارس 2021 ارائه شده است. در این روش جدید، سه روش ترکیب انفرادی مختلف شامل روش میانگین‌گیری، روش تنزیل ‌شده و روش انقباض برای ترکیب خروجی‌های پیش‌بینی مربوط به سه مدل ترکیبی متشکل از شبکه عصبی پرسپترون (MLPANN)  و الگوریتم‌های تکاملی (الگوریتم ژنتیک GA، الگوریتم ازدحام ذرات PSO و الگوریتم رقابت استعماری ICA) مورد استفاده قرار گرفتند. نتایج حاصل از این مطالعه نشان داد که بر اساس شاخص آماری RMSE، مدل ترکیبی پرسپترون- الگوریتم رقابت استعماری (MLPANN-GA) و روش انفرادی انقباضی با (K=0.25) دارای بالاترین دقت در پیش‌بینی قیمت گوشت گاو، گوسفند و مرغ است. همچنین عملکرد مدل پیشنهادی از اجزای آن (مدل‌های ترکیبی) بهتر است. روش پیشنهادی برای پیش‌بینی از نظر نوع محصول یا جایگزینی اجزای تشکیل‌دهنده دارای انعطاف‌پذیری است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A Flexible Combination Forecast Method for Modeling Agricultural Commodity Prices: A Case Study Iran’s ‎Livestock and Poultry Meat Market

نویسنده [English]

  • R. Heydari
Institute for Research in Planning, Agricultural Economics and Rural Development
چکیده [English]

In recent years, the fluctuation in agricultural commodity prices in Iran is increased and thus, accurate forecasting of price change is necessary. In this article, a flexible combined method in modeling monthly prices of beef, lamb and chicken from April 2001 to March 2021, was proposed. In this new method, three different approaches namely simple averaging, discounted and shrinkage methods were effectively used to combine the forecasting outputs of three hybrid methods (MLPANN-GA, MLPANN-PSO and MLPANN-ICA) together. In implementation stage of hybrid methods, based on test and error method, the optimal MLPANN structure was found with 3/2/4–6–1 architectures and the controlling parameters are carefully assigned. The results obtained from three hybrid methods indicate that, based on the RMSE statistical index, the MLPANN-ICA method performs the best when forecasting prices for beef, lamb, and chicken. The outputs of three combination approaches show that the shrinkage method, with a parameter value of K=0.25, achieves the highest prediction accuracy when forecasting prices for these three meats. In summary, the proposed method outperforms the other three hybrid methods overall.

کلیدواژه‌ها [English]

  • Agricultural commodity prices
  • Forecasting
  • Hybrid method
  • Meat

مراجع

  1. Agriculture Ministry of Iran, (2021). https://iranslal.com
  2. Ahmadi, M.A., Soleimani, R., Lee, M., Tomoaki Kashiwao, T., & Bahadori, A. (2015). Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool. Journal of Petroleum, 1, 118-132. http://dx.doi.org/10.1016/j.petlm.2015.06.004
  3. Ahumadaa, H., & Cornejo, M. (2016). Forecasting food prices: The case of corn, soybeans and wheat. International Journal of Forecasting, 32, 838-848. http://dx.doi.org/10.1016/j.ijforecast.2016.01.002
  4. Aiolfia, M., & Timmermann, A. (2006). Persistence in forecasting performance and conditional combination strategies. Journal of Econometrics, 135, 31-53.
  5. Ajmera, R., Kook, N., & Crilley, J. (2012). Impact of commodity price movements on CPI inflation. Monthly Labor Review, 29-43. http://www.jstor.org/stable/monthlylaborrev.2012.04.029
  6. Allen, R., Zivin, G., & Shrader, J. (2016). Forecasting in the presence of expectations. European. Physical Journal Special Topics, 225, 539-550.
  7. Amiri, M., Ghiasi-Freez, J., Golkar, B., & Hatampour, A. (2015). Improving water saturation estimation in a tight Shaly sandstone reservoir using artificial neural network optimized by imperialist competitive algorithm-A case study. Journal of Petroleum Science and Engineering, 127, 347-358.
  8. Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. 2007 IEEE Congress on Evolutionary Computation. IEEE, 4661-4667. http://dx.doi.org/10.1109/CEC.2007.4425083
  9. Atsalakis, G.S. (2014). Agriculture commodity prices forecasting using a fuzzy inference system. Journal of Agricultural Cooperative Management and Policy, 353-368.
  10. Chandrasekaran, M., & Tamang, S. (2017). ANN-PSO Integrated optimization methodology for intelligent control of MMC machining. Journal of Institution Engineers India Series C, 98(4): 395-401. https://doi.org/10.1007/s40032-016-0276-3
  11. Chen, P. (2015). Global oil prices, macroeconomic fundamentals and China's commodity sector comovements. Journal of Energy Policy, 87, 284-294.
  12. Chen, S., Wang, P.P., & Tzu-Wen Kuo, T. (2010). Computational intelligence in economics and finance: shifting the research frontier. Journal of New Mathematics and Natural Computation, 2(3), 1-23.
  13. Costantinia, M., & Pappalardob, C. (2010). A hierarchical procedure for the combination of forecasts. International Journal of Forecasting, 26, 725-743. https://doi.org/10.1016/j.ijforecast.2009.09.006
  14. Das, S.P., & Padhy, S. (2015). A novel hybrid model using teaching learning-based optimization and a support vector machine for commodity futures index forecasting. International Journal Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-015-0359-0
  15. Dreibus, T.C., Josephs, , & Jargon, J. (2014). Food prices surge as drought exacts a high toll on crops. Wall Street Journal. (www. Wsj.com/articles)
  16. (2022). World food and agriculture statistical pocketbook. Food and Agriculture Organization of the United Nations.
  17. Fowowe, B. (2016). Do oil prices drive agricultural commodity prices? Evidence from South Africa. Journal of Energy, 104, 149-157.
  18. Garganoa, A., & Timmermannb, A. (2013). Forecasting commodity price indexes using macroeconomic and financial predictors. International Journal of Forecasting, 30(3), 825-843.
  19. Gaur, Sh., Sudheer, Ch., Graillot, D., Chahar B.R., & Kumar, D.N. (2013). Application of artificial neural networks and particle swarm optimization for the management of groundwater resources. Journal of Water Resour Manage, 27, 927-941. https://doi.org/10.1007/s11269-0120226-7
  20. Hasan, M.M., Zahara, M.T., Sykot, M.M., Hafiz, R., & Saifuzzaman, M. (2020). Solvingonion market instability by forecasting onion price using machine learning approach. 2020 International Conference on Computational Performance Evaluation (ComPE), 777-
  21. Heddam, S. (2016). Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA. Journal of Environment Science Pollution Reserch, 23, 17210-17225. https://doi.org/10.1007/s11356-016-6905-9
  22. Hooshyaripor, F., Tahershamsi, A., & Behzadian, K. (2015). Estimation of peak outflow in dam failure using neural network approach under uncertainty analysis. Journal of Water Resources, 42(5), 721-734. https://doi.org/10.1134/S0097807815050085
  23. Hornik, K., Stinchombe, M., & White, H. (1989). Multi-layer feed forward networks are universal approximations. Journal of Neural Networks, 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  24. Jahed Armaghani, D., Tonnizam Mohamad, E., Narayanasamy, M.S., Narita, N., & Yagiz, S. (2017). Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Journal of Tunnelling and Underground Space Technology, 63, 29-43. http://dx.doi.org/10.1016/j.tust.2016.12.009
  25. Johns, J.M., & Burkes, D. (2017). Development of multilayer perceptron networks for isothermal time temperature transformation prediction of U-Mo-X alloys. Journal of Nuclear Materials, 490, 155-166. http://dx.doi.org/10.1016/j.jnucmat.2017.03.050
  26. Kantanantha, N., Serban, N., & Griffin, P. (2010). Yield and price forecasting for stochastic crop decision planning. Journal of Agricultural, Biological, and Environmental Statistics, 15(3), 362-380.
  27. Karimi, H., & Yousefi, (2012). Application of artificial neural network-genetic algorithm (ANN-GA) to correlation of density in Nanofluids. Journal of Fluid Phase Equilibria, 336, 79-83.
  28. Kartheeswaran, S., & Christopher Durairaj, D.D. (2017). A data-parallelism approach for PSO-ANN based medical image reconstruction on a multi-core system. Informatics in Medicine Unlocked 8, 1-11. http://dx.doi.org/10.1016/j.imu.2017.05.001
  29. Khandelwal, M., Mahdiyar, , Jahed Armaghani, D., Singh, T.N., Fahimifar, A., & Shirani Faradonbeh, R. (2017). An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals. Journal of Environment Earth Science, 76, 399. https://doi.org/10.1007/s12665-017-6726-2
  30. Kisi, O., Alizamir, M., & Zounemat-Kermani, M. (2017). Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data. Journal of Natural Hazards, 87, 367-381. https://doi.org/10.1007/s11069-017-2767-9
  31. Lazzus, J.A. (2011). Autoignition temperature prediction using an artificial neural network with particle swarm optimization. International Journal of Thermophys, 32, 957-973. https://doi.org/10.1007/s10765-011-0956-4
  32. Mohamed, M.M., & Al-Mualla, A.A. (2010). Water demand forecasting in umm Al-Quwain (UAE) using the IWR-MAIN specify forecasting model. Journal of Water Resource Management, 24, 4093-4120.
  33. Mohammadi Ghahdarijani, A., Hormozi, F., & Haghighi Asl, A. (2017). Convective heat transfer and pressure drop study on nanofluids in double-walled reactor by developing an optimal multilayer perceptron artificial neural network. Journal of International Communications in Heat and Mass Transfer, 84, 11-19. http://dx.doi.org/10.1016/j.icheatmasstransfer.2017.03.014
  34. Mollaiy-Berneti, Sh. (2015). Developing energy forecasting model using hybrid artificial intelligence method, Journal of Central South University, 22, 3026-3032. https://doi.org/10.1007/s11771-015 2839-5
  35. Nazlioglu, S. (2011). World oil and agricultural commodity prices: Evidence from nonlinear causality. Journal of Energy Policy, 39, 2935-2943.
  36. Nazlioglu, S., & Soytas, U. (2011). World oil prices and agricultural commodity prices: Evidence from an emerging market. Journal of Energy Economics, 33, 488-496.
  37. No, S.Ch., & Salassi, M.E. (2009). A sequential rationality test of USDA preliminary price estimates for selected program crops: rice, soybeans, and wheat. Journal of International Advances Economic Research, 15, 470-482.
  38. Nosratabadi, S., Szell. K., Beszedes, B., Imre, F., Ardabili, S., & Mosavi, A. (2020). Hybrid machine learning models for crop yield prediction. Journal of Computer Science, Neural and Evolutionary Computing, 1-5.
  39. Obe, O.O., & Shangodoyin, D.K. (2016). Artificial neural network based model for forecasting sugar cane production. Journal of Computer Science, 6(4), 439-445.
  40. Pannakkong, W., Huynh, V., & Sriboonchitta, S. (2016). ARIMA versus artificial neural network for Thailand’s Cassava starch export forecasting. International Publishing Switzerland 2016. Studies in Computational Intelligence, 622, 255-277. https://doi.org/10.1007/978-3-319-27284-9_16
  41. Pham Dieu, B.Th., Bui, , Prakash, I., & Dholakia, M.B. (2017). Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Journal of Catena, 149, 52-63. http://dx.doi.org/10.1016/j.catena.2016.09.007
  42. Pokterng, S., & Kengpol, A. (2007). The forecasting of durian production quantity for consumption in domestic and international markets. KMUTNB: International Journal of Applied Science Technoloy, 3(3), 7-18.
  43. Raflesia, S.P., Taufiqurrahman, , Iriyani, S., & Lestarini, D. (2021). Agricultural commodity price forecasting using PSO-RBF neural network for farmers exchange rate improvement in Indonesia. Indonesian Journal of Electrical Engineering and Informatics, 9(3), 784-792.
  44. Raikar, R.V., Wang, Ch. Y., Shih, H., & Hong, J. (2016). Prediction of contraction scour using ANN and GA. Journal of Flow Measurement and Instrumentation, 50, 26-34. http://dx.doi.org/10.1016/j.flowmeasinst.2016.06.006
  45. Rapach, D.E., & Strauss, J.K. (2009). Differences in housing price forecastability across US states. International Journal of Forecasting, 25, 351-372. https://doi.org/10.1016/j.ijforecast.2009.01.009
  46. Sangwan, K.S., Saxena, , & Kanta, G. (2015). Optimization of machining parameters to minimize surface roughness using integrated ANN-GA approach. Journal of Procedia CIRP, 29, 305-310. http://creativecommons.org/licenses/by-nc-nd/4.0/
  47. Shahwan, T., & Odening, M. (2007). Forecasting agricultural commodity prices using hybrid neural networks. Journal of Computational Intelligence in Economics and Finance, Berlin, 63-74.
  48. Shao, Y.E., & Dai, J.T. (2018). Integrated feature selection of ARIMA with computational intelligence approaches for food crop price prediction. Complexity.
  49. Shojaie, A.A., Dolatshahi Zand, A., & Vafaie, Sh. (2016). Calculating production by using short term demand forecasting models: a case study of fuel supply system. Journal of Evolving Systems. https://doi.org/10.1007/s12530-016-9173-5
  50. Stock, J.H., & Watson, W.M. (2004). Combination forecasts of output growth in a seven-country data set. Journal of Forecasting, 23, 405-430. https://doi.org/10.1002/for.928
  51. Tian, F., Yang, K., & Chen, L. (2017). Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity. International Journal of Forecasting, 33, 132-152. http://dx.doi.org/10.1016/j.ijforecast.2016.08.002
  52. Ticlavilca, A.M., Feuz, D.M., & McKee, M. (2010). Forecasting agricultural commodity prices using multivariate Bayesian machine learning regression. The NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management St. Louis, Missouri, April 19-20, 2010.
  53. Timmer, C.P. (2014). Food Security, Market Processes, and the Role of Government Policy, Encyclopedia of Agriculture and Food Systems. Elsevier Ltd. https://doi.org/10.1016/B978-0-444-52512-3.00033-4
  54. Tomek, W.G., & Kaiser, M. (2014). Price variation through time. Cornell University Press. http://www.jstor.org/stable/10.7591/j.ctt5hh0j8.13
  55. Wang, B., Liu, P., Chao, Z., Junmei, W., Chen, W., Cao, N., O’Hare, G.M.P., & Wen, F. (2018). Research on hybrid model of garlic short-term price forecasting based on big data. Journal of Computers, Materials and Continua (CMC), 57(2), 283-296.
  56. Weng, Y., Wang, X., Hua, , Wang, H., Kang, M., & Wang, F.Y. (2019). Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by web crawler. Journal of IEEE Transactions on Computational Social Systems, 6(3), 547-553.
  57. Wihartiko, F.D., Nurdiati, S., Buono, A., & Santosa, E. (2021). Agricultural price prediction models: a systematic literature review. International Conference on Industrial Engineering and Operations Management Singapore, March 7-11: 2927-2934.
  58. Wu, H., Wu, H., Zhu, M., Chen, , & 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. https://doi.org/10.1186/s40537-016-0062-3
  59. Xiong, T., Li, Ch., Bao, Y., Hu, , & Zhang, L. (2015). A combination method for interval forecasting of agricultural commodity futures prices. Journal of Knowledge-Based Systems, 77, 92-102.
  60. 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. http://dx.doi.org/10.1016/j.asoc.2016.07.053
  61. Ye, L., Li, Y., Liu, Y., Qin, , & Liang, W. (2014). Research on the optimal combination forecasting model for vegetable price in Hainan. Proceedings of 2013 World Agricultural Outlook Conference, Springer-Verlag Berlin Heidelberg 2014.
  62. Zou, H., Xia, G., Yang, F., & Wang, (2007). An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Journal of Neuro computing, 70(16), 2913–2923.

 

 

ارسال نظر در مورد این مقاله
CAPTCHA Image

فایل ها

سابقه مقاله

  • تاریخ دریافت: 05 اردیبهشت 1402
  • تاریخ بازنگری: 26 تیر 1402
  • تاریخ پذیرش: 26 تیر 1402

هم رسانی

ارجاع به این مقاله

آمار