Submit

Bhartiya Krishi Anusandhan Patrika, volume 34 issue 1 (march 2019) : 33-37

Potato price analysis of Delhi market through ensemble empirical mode decomposition 

Kapil Choudhary, Girish Kumar Jha, Rajeev Ranjan Kumar
1<p>ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110 012</p>
Cite article:- Choudhary Kapil, Jha Kumar Girish, Kumar Ranjan Rajeev (NaN). Potato price analysis of Delhi market through ensemble empirical mode decomposition . Bhartiya Krishi Anusandhan Patrika. 34(1): 33-37. doi: 10.18805/BKAP147.

ABSTRACT

Agricultural commodities prices depends on production, unnecessary demand, production uncertainty, market flaws etc. Due to these factors agricultural price series are non-stationary and non-linear in nature. Therefore analyzing agricultural commodities prices is considered as a challenging task. The traditional stationary approach of time series is unable to capture non-stationary and non-linear properties of agricultural price series. Non-stationary and non-linear properties present in the price series may be accurately analyzed through empirical mode decongation (EMD). In this technique, the original time series decomposed into intrinsic mode functions and residue. One of the major limitation of EMD is the presence of the mode mixing. To overcome this limitation of the EMD, we use ensemble empirical mode decomposition (EEMD). Using this technique in this study, Delhi market potato prices have been analyzed.

KEYWORDS

REFERENCES


  1. Allen, P. G. (1994). Economic forecasting in agriculture. International Journal of Forecasting, 10, 81-135.

  2. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., and Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London A: mathematical, physical and engineering sciences. 454,903-    995.

  3. Jha, Girish K. and Sinha, K. (2013). Agricultural price forecasting using neural network model: An innovative information delivery system. Agricultural Economics Research Review, 26(2), 229-239.

  4. Lin, C. S., Chiu, S. H., and Lin, T. Y. (2012). Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting. Economic Modelling, 29(6), 2583-2590.

  5. Yu, L., Wang, Z., and Tang, L. (2015). A decomposition–    ensemble model with data-characteristic-    driven reconstruction for crude oil price forecasting. Applied Energy,156, 251-267.

  6. Wu, Z. and Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis,1(1), 1-41.

  7. Zhang, X., Lai, K. K., and Wang, S. Y. (2008). A new approach for crude oil price analysis based on empirical mode decomposition. Energy economics,30(3), 905-918.

  8. Zhang, J. L., Zhang, Y. J., and Zhang, L. (2015). A novel hybrid method for crude oil price forecasting. Energy Economics,49, 649-659.

  9. Zhu, B. (2012). A novel multiscale ensemble carbon price prediction model integrating empirical mode decomposition, genetic algorithm and artificial neural network. Energies, 5(2), 355-370.

  10. Zhu, B., Shi, X., Chevallier, J., Wang, P., and Wei, Y. M. (2016). An adaptive multiscale ensemble earning paradigm for nonstationary and nonlinear energy price time series forecasting. Journal of Forecasting,35(7), 633-651. 
Reviewed By

Download Article
In this Article
    Contact us
    Follow us

    Editorial Board

    View all (0)