×

Grain price forecasting using a hybrid stochastic method. (English) Zbl 1377.91097

Summary: Grain price forecasting is significant for all market participants in managing risks and planning operations. However, precise forecasting of price series is difficult because of the inherent stochastic behavior of grain price. In this paper, a novel hybrid stochastic method for grain price forecasting is introduced. The proposed method combines decomposed stochastic time series processes with artificial neural networks. The initial parameters of the hybrid stochastic model are optimized by a random search using a genetic algorithm. The proposed method is finally validated in China’s national grain market and compared with several recent price forecasting models. Results indicate that the proposed hybrid stochastic method provides a satisfactory forecasting performance in grain price series.

MSC:

91B84 Economic time series analysis
62P20 Applications of statistics to economics
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Amjady, N and Hemmati, M (2009). Day-ahead price forecasting of electricity markets by a hybrid intelligent system. European Transactions on Electrical Power, 19(1), 89-102.
[2] Amjady, N and Keynia, F (2008). Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method. International Journal of Electrical Power and Energy Systems, 30(9), 533-546.
[3] Antanasijevi, DZ, Pocajt, VV, Povrenovi, DS, Risti, M and Peri-Gruji, AA (2013). Pm10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Science of The Total Environment, 443, 511-519.
[4] Aye, G, Gupta, R, Hammoudeh, S and Kim, WJ (2015). Forecasting the price of gold using dynamic model averaging. International Review of Financial Analysis, 41, 257-266.
[5] Box, GEP and Pierce, DA (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American statistical Association, 65(332), 1509-1526. · Zbl 0224.62041
[6] Bradshaw, GW and Orden, D (1990). Granger causality from the exchange rate to agricultural prices and export sales. Western Journal of Agricultural Economics, 15(1), 100-110.
[7] Chabane, N (2014). A hybrid arfima and neural network model for electricity price prediction. International Journal of Electrical Power and Energy Systems, 55, 187-194.
[8] Chang, PC and Fan, CY (2008). A hybrid system integrating a wavelet and tsk fuzzy rules for stock price forecasting. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(6), 802-815.
[9] Collins, A and Evans, A (1994). Aircraft noise and residential property values: An artificial neural network approach. Journal of Transport Economics and Policy, 28(2), 175-197.
[10] Conejo, AJ, Plazas, MA, Espinola, R and Molina, AB (2005). Day-ahead electricity price forecasting using the wavelet transform and arima models. IEEE Transactions on Power Systems, 20(2), 1035-1042.
[11] Crisostomi, E, Gallicchio, C, Micheli, A, Raugi, M and Tucci, M (2015). Prediction of the italian electricity price for smart grid applications. Neurocomputing, 170, 286-295.
[12] Engle, RF (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica: Journal of the Econometric Society, 50(4), 987-1007. · Zbl 0491.62099
[13] Freebairn, JW (1975). Forecasting for australian agriculture. Australian Journal of Agricultural Economics, 19(3), 154-174.
[14] Haigh, MS and Holt, MT (2000). Hedging multiple price uncertainty in international grain trade. American Journal of Agricultural Economics, 82(4), 881-896.
[15] Haofei, Z, Guoping, X, Fangting, Y and Han, Y (2007). A neural network model based on the multi-stage optimization approach for short-term food price forecasting in China. Expert Systems with Applications, 33(2), 347-356.
[16] Haupt, RL and Haupt, SE (2004). Practical Genetic Algorithms.NJ, USA: John Wiley & Sons. · Zbl 1072.68089
[17] Haykin, SS, Haykin, SS, Haykin, SS and Haykin, SS (2009). Neural Networks and Learning Machines.NJ, USA: Pearson. · Zbl 0934.68076
[18] Hettiarachchi, H and Banneheka, B (2013). Time series regression and artificial neural network approaches for forecasting unit price of tea at colombo auction. Journal of the National Science Foundation of Sri Lanka, 41(1), 35-40.
[19] Hu, MJC (1964). Application of the Adaline System to Weather Forecasting, PhD thesis, Stanford University.
[20] Jammazi, R and Aloui, C (2012). Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling. Energy Economics, 34(3), 828-841.
[21] Jiajun, Z and Quanyin, Z (2012). Price forecasting for agricultural products based on bp and rbf neural network. in IEEE Int. Conf. on Computer Science and Automation Engineering, pp. 607-610.
[22] Kohzadi, N, Boyd, MS, Kermanshahi, B and Kaastra, I (1996). A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing, 10(2), 169-181. · Zbl 0850.90008
[23] Kroner, KF, Kneafsey, KP and Claessens, S (1995). Forecasting volatility in commodity markets. Journal of Forecasting, 14(2), 77-95.
[24] Li, W, Zuo, MJ, and Moghaddass, R (2011). Optimal design of a multi-state weighted series-parallel system using physical programming and genetic algorithms, Asia Pacific Journal of Operational Research, 28(4), 543-562. · Zbl 1233.90131
[25] Lianghsuan, C and Chenghsiung, C (2011). Multi-objective optimization in reliability system using genetic algorithm and neural network. Asia Pacific Journal of Operational Research, 25(5), 649-672.
[26] Liu, B, Duan, T and Li, Y (2009). One improved agent genetic algorithm ring-like agent genetic algorithm for global numerical optimization. Asia Pacific Journal of Operational Research, 26(4), 479-502. · Zbl 1172.65361
[27] McCann, PJ and Kalman, BL (1994). A neural network model for the gold market. J Forecast, 16, 165-176.
[28] McCulloch, WS and Pitts, W (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of Mathematical Biophysics, 5(4), 115-133. · Zbl 0063.03860
[29] Mishra, GC and Singh, A (2013). A study on forecasting prices of groundnut oil in delhi by arima methodology and artificial neural networks. Agris on-line Papers in Economics and Informatics, 5(3), 25-34.
[30] Myer, GL and Yanagida, JF (1984). Combining annual econometric forecasts with quarterly arima forecasts: A heuristic approach. Western Journal of Agricultural Economics, 9(1), 200-206.
[31] Myers, RJ (1986) Conditional heteroscedastic error processes and the time pattern of optimal hedge ratios. in Proc. NCR-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management, pp. 310-324. St. Louis, MO, .
[32] Nghiep, N and Al, C (2009). Predicting housing value: A comparison of multiple regression analysis and artificial neural networks. Journal of Real Estate Research, 22(3), 313-336.
[33] Parisi, A, Parisi, F and Daz, D (2008). Forecasting gold price changes: Rolling and recursive neural network models. Journal of Multinational Financial Management, 18(5), 477-487.
[34] Plakandaras, V, Gupta, R, Gogas, P and Papadimitriou, T (2015). Forecasting the u.s. real house price index. Economic Modelling45, 259-267.
[35] Ramsey, JB (1999). The contribution of wavelets to the analysis of economic and financial data. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 357(1760), 2593-2606. · Zbl 0963.91506
[36] Ramsey, JB and Zhang, Z (1997). The analysis of foreign exchange data using waveform dictionaries. Journal of Empirical Finance, 4(4), 341-372.
[37] Schmitz, A (2010). Agricultural Policy, Agribusiness, and Rent-seeking Behavior.Toronto, Canada: University of Toronto Press.
[38] Shouyang, W, Lean, Y and Lai, KK (2005). Crude oil price forecasting with tei@i methodology. Journal of Systems Science and Complexity, 18(2), 145-166. · Zbl 1121.91340
[39] Snyder, J, Sweat, J, Richardson, M and Pattie, D (1992). Developing neural networks to forecast agricultural commodity prices, in Hawaii International Conference on System Sciences, pp. 516-522IEEE: Kauai, Hawaii.
[40] Szkuta, BR, Sanabria, LA and Dillon, TS (1999). Electricity price short-term forecasting using artificial neural networks. IEEE Transactions on Power Systems, 14(3), 851-857.
[41] Valente, JMS, Fernandogonalves, J and Rui, AFSA (2006). A hybrid genetic algorithm for the early/tardy scheduling problem. Asia Pacific Journal of Operational Research, 23(3), 393-405. · Zbl 1103.90050
[42] Wang, FK, Chang, KK and Tzeng, CW (2011). Using adaptive network-based fuzzy inference system to forecast automobile sales. Expert Systems with Applications, 38(8), 10587-10593.
[43] Weron, R (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030-1081.
[44] Williams, D and Hinton, GE (1986). Learning representations by back-propagating errors. Nature, 323, 533-536. · Zbl 1369.68284
[45] Wu, WH, Yin, Y, Cheng, SR, Hsu, PH and Wu, CC (2014). Genetic algorithm for a two-agent scheduling problem with truncated learning consideration. Asia Pacific Journal of Operational Research, 31(6), 1450046. · Zbl 1307.90077
[46] Xu, J, Huang, E, Chen, CH and Lee, LH (2015). Simulation optimization: A review and exploration in the new era of cloud computing and big data. Asia-Pacific Journal of Operational Research, 32(03), 1550019. · Zbl 1318.68186
[47] Xu, J, Zhang, S, Huang, E, Chen, CH, Lee, LH and Celik, N (2016). Mo2tos: Multi-fidelity optimization with ordinal transformation and optimal sampling. Asia Pacific Journal of Operational Research, 33(03), 1650017. · Zbl 1345.90047
[48] Yamin, HY, Shahidehpour, SM and Li, Z (2004). Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets. International Journal of Electrical Power and Energy Systems, 26(8), 571-581.
[49] Yu, L, Wang, S and Lai, KK (2008). Forecasting crude oil price with an emd-based neural network ensemble learning paradigm. Energy Economics, 30(5) 2623-2635.
[50] Zhang, GP (2003). Time series forecasting using a hybrid arima and neural network model. Neurocomputing, 50, 159-175. · Zbl 1006.68828
[51] Zhang, J, Tan, Z and Yang, S (2012). Day-ahead electricity price forecasting by a new hybrid method. Computers and Industrial Engineering, 63(3), 695-701.
[52] Zhang, JL, Zhang, YJ and Zhang, L (2015). A novel hybrid method for crude oil price forecasting. Energy Economics, 49, 649-659.
[53] Zou, HF, Xia, GP, Yang, FT and Wang, HY (2007). An investigation and comparison of artificial neural network and time series models for chinese food grain price forecasting. Neurocomputing, 70(1618), 2913-2923.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.