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

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.