Comparative Study of EMD based Modelling Techniques for Improved Agricultural Price Forecasting

Abstract

Forecasting agricultural commodity prices is regarded as a challenging task due to its non-linear and non-stationary nature. As agriculture productionis highly reliant on various biological and agro-meteorological factors, traditional smoothing techniques as well as statistical models often fail tomodel such series satisfactorily. To capture such complex patterns effectively, different data-driven and self-adaptive techniques have been developedtime-to-time. Against this backdrop, in this paper, we have assessed the suitability of empirical mode decomposition (EMD)-based neural network andsupport vector regression (SVR) approaches for forecasting wholesale prices of three major potato markets namely, Agra, Bangalore, and Mumbai.As the benchmark models, autoregressive integrated moving average (ARIMA), time delay neural network (TDNN) and SVR models have beenemployed for the comparative evaluation. The experimental results clearly reveal the comparative superiority of the EMD-SVR model for the Agraand Bangalore markets and the EMD-TDNN model for the Mumbai market in terms of root mean squared error values and turning point predictions.Moreover, all the EMD-based models have performed better than the other competing models.