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

Abstract

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 problems 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.