An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China T., Li C., Bao Y. (2017): An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China. Agric. Econ. – Czech, 63: 136-148.
download PDF
Short-term forecasting of hog price, which forms the basis for the decision making, is challenging and of great interest for hog producers and market participants. This study develops improved ensemble empirical mode decomposition (EEMD)-based hybrid approach for the short-term hog price forecasting. Specifically, the EEMD is first used to decompose the original hog price series into several intrinsic-mode functions (IMF) and one residue. The fine-to-coarse reconstruction algorithm is then applied to compose the obtained IMFs and residue into the high-frequency fluctuation, the low-frequency fluctuation, and the trend terms which can highlight new features of the hog price fluctuations. Afterwards, the extreme learning machine (ELM) is employed to model the low-frequency fluctuation, while the autoregressive integrated moving average (ARIMA) and the polynomial function are used to fit the high-frequency fluctuation and trend term, respectively, in a multistep-ahead fashion. The commonly used iterated prediction strategy is adopted for the implementation of the multistep-ahead forecasting. The monthly hog price series from January 2000 to May 2015 in China is employed to evaluate the forecasting performance of the proposed approach with the selected counterparts. The numerical results indicate that the improved EEMD-based hybrid approach is a promising alternative for the short-term hog price forecasting.  
Adanacioglu H., Yercan M. (2012): An analysis of tomato prices at wholesale level in Turkey: an application of SARIMA model. Custos e@ gronegócio on line. 8: 52–75.
Box G.E., Jenkins G.M. (1976): Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco.
Chang Chih-Chung, Lin Chih-Jen (2011): LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2, 1-27
Chen Chun-Fu, Lai Ming-Cheng, Yeh Ching-Chiang (2012): Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge-Based Systems, 26, 281-287
Diebold F.X., Mariano R.S. (1995): Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 20: 134–144.
Felipe I.J., Mol A.L., Almeida V. (2012): Application of ARIMA models in soybean series of prices in the north of Paraná. Custos e@ gronegócio Online, 8: 78–91.
Guo Zhenhai, Zhao Weigang, Lu Haiyan, Wang Jianzhou (2012): Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renewable Energy, 37, 241-249
Hahn W.F. (2004): Beef and pork values and price spreads explained. Electronic Outlook Report from Eonomic Research Service, USDA.
Guang-Bin Huang , Babri H.A. (): Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Transactions on Neural Networks, 9, 224-229
Guang-Bin Huang (2003): Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Transactions on Neural Networks, 14, 274-281
Huang G.-B., Chen L., Siew C.-K. (2006): Universal Approximation Using Incremental Constructive Feedforward Networks With Random Hidden Nodes. IEEE Transactions on Neural Networks, 17, 879-892
Huang Guang-Bin, Zhu Qin-Yu, Siew Chee-Kheong (2006): Extreme learning machine: Theory and applications. Neurocomputing, 70, 489-501
Huang N. E., Shen Z., Long S. R., Wu M. C., Shih H. H., Zheng Q., Yen N.-C., Tung C. C., Liu H. H. (1998): The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454, 903-995
Hyndman R.J., Khandakar Y. (2007): Automatic Time Series for Forecasting: the Forecast Package for R. Department of Econometrics and Business Statistics, Monash University.
Jha Girish K., Sinha Kanchan (2014): Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India. Neural Computing and Applications, 24, 563-571
Jumah A., Kunst R.M. (2008): Seasonal prediction of European cereal prices: good forecasts using bad models? Journal of Forecasting, 27: 391–406.
Kisi Ozgur, Latifoğlu Levent, Latifoğlu Fatma (2014): Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series. Water Resources Management, 28, 4045-4057
LI Gan-qiong, XU Shi-wei, LI Zhe-min, SUN Yi-guo, DONG Xiao-xia (2012): Using Quantile Regression Approach to Analyze Price Movements of Agricultural Products in China. Journal of Integrative Agriculture, 11, 674-683
LI Zhe-min, CUI Li-guo, XU Shi-wei, WENG Ling-yun, DONG Xiao-xia, LI Gan-qiong, YU Hai-peng (2013): Prediction Model of Weekly Retail Price for Eggs Based on Chaotic Neural Network. Journal of Integrative Agriculture, 12, 2292-2299
Martín-Rodríguez Gloria, Cáceres-Hernández José Juan (2012): Forecasting pseudo-periodic seasonal patterns in agricultural prices. Agricultural Economics, 43, 531-544
Napolitano Giulia, Serinaldi Francesco, See Linda (2011): Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: An empirical examination. Journal of Hydrology, 406, 199-214
Paul R.K., Gurung B., Paul A. (2015): Modelling and forecasting of retail price of arhar dal in Karnal, Haryana. The Indian Journal of Agricultural Sciences, 85.
Ramirez O.A., Fadiga M. (2003): Forecasting agricultural commodity prices with asymmetric-error GARCH models. Journal of Agricultural and Resource Economics, 28: 71–85.
Ribeiro Celma O., Oliveira Sydnei M. (2011): A hybrid commodity price-forecasting model applied to the sugar-alcohol sector. Australian Journal of Agricultural and Resource Economics, 55, 180-198
Saengwong S., Jatuporn C., Roan S. (2012): An analysis of Taiwanese livestock prices: empirical time series approaches. Journal of Animal and Veterinary Advances. 11: 4340–4346.
Sharma A. (2000): Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification. Journal of Hydrology, 239, 232-239
Shih M.L., Huang B.W., Chiu Nan-Hsing, Chiu C., Hu W.Y. (2009): Farm price prediction using case-based reasoning approach—A case of broiler industry in Taiwan. Computers and Electronics in Agriculture, 66, 70-75
Shrivastava N.A., Panigrahi B.K. (2014): A hybrid wavelet-ELM based short term price forecasting for electricity markets. International Journal of Electrical Power & Energy Systems, 55: 41–50.
Su Xin, Wang Yi, Duan Shengsen, Ma Junhai (2014): Detecting Chaos from Agricultural Product Price Time Series. Entropy, 16, 6415-6433
Ben Taieb Souhaib, Bontempi Gianluca, Atiya Amir F., Sorjamaa Antti (2012): A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Systems with Applications, 39, 7067-7083
Xiong Tao, Bao Yukun, Hu Zhongyi (2014): Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting. Knowledge-Based Systems, 55, 87-100
Xiong Tao, Li Chongguang, Bao Yukun, Hu Zhongyi, Zhang Lu (2015): A combination method for interval forecasting of agricultural commodity futures prices. Knowledge-Based Systems, 77, 92-102
Yu Lean, Wang Shouyang, Lai Kin Keung (2008): Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, 30, 2623-2635
Yu Lean, Wang Zishu, Tang Ling (2015): A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting. Applied Energy, 156, 251-267
Yu Lean, Dai Wei, Tang Ling (2016): A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting. Engineering Applications of Artificial Intelligence, 47, 110-121
Xiaoshuan Zhang, Tao Hu, Revell Brain, Zetian Fu (2005): A forecasting support system for aquatic products price in China. Expert Systems with Applications, 28, 119-126
Zhang Xun, Lai K.K., Wang Shou-Yang (2008): A new approach for crude oil price analysis based on Empirical Mode Decomposition. Energy Economics, 30, 905-918
Zhou Qingping, Jiang Haiyan, Wang Jianzhou, Zhou Jianling (2014): A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Science of The Total Environment, 496, 264-274
Zhu Qin-Yu, Qin A.K., Suganthan P.N., Huang Guang-Bin (2005): Evolutionary extreme learning machine. Pattern Recognition, 38, 1759-1763
download PDF

© 2020 Czech Academy of Agricultural Sciences