Agricultural commodity price analysis using ensemble empirical mode decomposition: A case study of daily potato price series

KAPIL CHOUDHARY; GIRISH K JHA; RAJEEV R KUMAR; DWIJESH C MISHRA

doi:10.56093/ijas.v89i5.89682

Authors

KAPIL CHOUDHARY Ph D Scholar, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
GIRISH K JHA Principal Scientist, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
RAJEEV R KUMAR Scientist, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
DWIJESH C MISHRA Scientist, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India

https://doi.org/10.56093/ijas.v89i5.89682

Keywords:

Agricultural commodity price analysis, Ensemble empirical mode decomposition, Fine-tocoarse reconstruction, Intrinsic mode functions

Abstract

Due to multifaceted nature of agricultural price series, conventional mono-scale smoothing approaches are unable to catch its nonstationary and nonlinear properties. Recently, empirical mode decomposition (EMD) has been proposed as a new tool for time-frequency analysis method, which adaptively represents nonstationary signals as sum of different components. The essence of EMD is to decompose a time series into a sum of intrinsic mode function (IMF) components with individual intrinsic time scale properties. One of the major drawbacks of the EMD is the frequent appearance of mode mixing. Ensemble EMD (EEMD) is a substantial improvement of EMD which can better separate the scales naturally by adding white noise series to the original time series and then treating the ensemble averages as the true intrinsic modes. In this paper, daily price data of potato in Bangalore and Delhi markets are decomposed into eight independent intrinsic modes and one residue with different frequencies, indicating some interesting features of price volatility. Further, decomposed IMFs and residue obtained through EEMD are grouped into high frequency, low frequency and a trend component which has similar frequency characteristics, using the fine-to-coarse reconstruction algorithm. These IMF and residue can be used for prediction using any traditional or artificial intelligence technique.

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