“EMD-SVR” Hybrid Machine Learning Model and its Application in Agricultural Price Forecasting

DOI: 10.18805/BKAP385    | Article Id: BKAP385 | Page : 1-7
Citation :- “EMD-SVR” Hybrid Machine Learning Model and its Application in Agricultural Price Forecasting.Bhartiya Krishi Anusandhan Patrika.2022.(37): 1-7
Pankaj Das, Girish Kumar Jha, Achal Lama, Bharti pankaj.iasri@gmail.com
Address : ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110 012, India.
Submitted Date : 13-10-2021
Accepted Date : 22-03-2022


Timely and accurate price forecasting is one of challenges in agriculture. It helps both producer and consumer to make the efficient plan. The inherent nonstationarity and nonlinearity in price data makes problem in forecasting. A single forecasting model may not be able to tackle nonstationarity and nonlinearity, simultaneously. With this context, a nonlinear hybrid model called EMD-SVR has been proposed to deal the problem. The empirical mode decomposition (EMD) deals with nonstationarity by decomposing price data into a finite and small number of subsets. Further, these decomposed subsets are forecasted using Support Vector Regression (SVR) model and aggregated to make final forecast. The performance of the proposed hybrid model are evaluated in monthly price index of chili. The empirical results indicated the superiority of the EMD-SVR model.


Agricultural price forecasting Empirical mode decomposition Nonlinearity Nonstationary Support vector regression.


  1. An, X., Jiang, D., Zhao, M. and Liu, C. (2012). Short time prediction of wind power using EMD and chaotic theory. Communication in Nonlinear Science and Numerical Simulation. 17(2): 1036-1042. 
  2. Anjaly, K.N., Surendran, S., Babu, S.K. and Thomas, J.K. (2010). Impact Assessment of Price Forecast: A Study of Cardamom Price Forecast by AMIC, KAU. NAIP on Establishing and Networking of Agricultural Market Intelligence Centres in India. College of Horticulture, Vellanikkara. 31.
  3. Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics. 31: 307-327.
  4. Brandl, B., Wildburger, U. and Pickl, S. (2009). Increasing of the fitness of fundamental exchange rate forecast models. International Journal of Contemporary Mathematical Sciences. 4(16): 779-798.
  5. Brock, W.A., Scheinkman, J.A., Dechert, W.D. and LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews. 15: 197-235.
  6. Chen, C.F., Lai, M. and Yeh, C.C. (2012). Forecasting tourism demand based on empirical mode decomposition. Knowledge-Based Systems. 26: 281-287. 
  7. Chen, S.Y. (2007). Forecasting exchange rates: A new nonparametric support vector regression. The Journal of Quantitative and Technical Economics. 5: 142-150.
  8. Das, P., Jha, G.K., Lama, A., Parsad, R. and Mishra, D. (2020). Empirical Mode Decomposition based Support Vector Regression for Agricultural Price Forecasting. Indian Journal of Extension Education. 56 (2): 7-12. (http://krishi.icar.gov.in/ jspui/handle/123456789/44138).
  9. Das, P., Lama, A. and Jha, G.K. (2021). R Package EMDSV Rhybrid. (http://krishi.icar.gov.in/jspui/ handle/123456789/44898).
  10. Duan, W.Q. and Stanley, H.E. (2011). Cross-correlation and predictability of financial return series. Physica A. 390(2): 290-296.
  11. Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of U.K. inflation. Econometrica. 50: 987-1008.
  12. Guo, Z., Zhao, W., Lu, H. and Wang, J. (2012). Multi- step forecasting for wind speed using a modified EMD-based artificial neural network model. Renewable Energy. 37(1): 241-249.
  13. Huang, N.E., Shen, Z., Long, S.R., Wu, M.L., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C. and Liu, H.H. (1998). The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis. Proceeding of the Royal Society London A. 454: 909-995.
  14. Ince, H. and Trafalis, T. (2006). A hybrid model for exchange rate prediction. Decision Support Systems. 42(2): 1054-1062.
  15. Lama, A., Jha, G., Gurung, B., Paul, R.K., Bharadwaj, A. and Parsad, R. (2016). A Comparative Study on Time-delay Neural Network and GARCH Models for Forecasting Agricultural Commodity Price Volatility. Journal of the Indian Society of Agricultural Statistics. 70(1): 7-18.
  16. Lu, C.J., Lee, T.S. and Chiu, C.C. (2009). Financial time series forecasting using independent component analysis and support vector machine. Decision Support Systems. 47(2): 115-125.
  17. Sugiyama, M. and Kawanabe, M. (2012). Machine Learning in Non-Stationary Environments- Introduction to Covariate Shift Adaptation. The MIT Press, Cambridge, Massachusetts, London, England. 2nd ed.
  18. Suykens, J.A.K. and Vandewalle, J. (1999). Least squares support vector machine classifier. Neural Processing Letters. 9(3): 293-300.
  19. Vladimir, N. Vapnik. (1998). Statistical Learning Theory. Wiley-Interscience. 1st ed.
  20. Zhang, G.P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 50: 159-175.

Global Footprints