Time Series Forecasting of Global Price of Soybeans using a Hybrid SARIMA and NARNN Model

Abstract

Global price of soybeans has a big impact because of the trade war between the U.S. and China. Under this circumstance, price forecast is vital to facilitate efficient decisions and will play a major role in coordinating the supply and demand of soybeans globally. Hence, the primary purpose of this study was to demonstrate the role of time series models in predicting process using the time series data of monthly global price of soybeans from January 1990 to January 2021. The SARIMA and NARNN models are good at modelling linear and nonlinear problems for the time series, respectively. However, using the hybrid model, a combination of the SARIMA and NARNN models has both linear and nonlinear modelling capabilities, can be a better choice for modelling the time series. The comparative results revealed that the Hybrid-LM model with 8 neurons in the hidden layer and 3 time delays yielded higher accuracy than the NARNN-LM model with 8 neurons in the hidden layer and 3 time delays, and the SARIMA, ARIMA(0,1,3)(0,0,2)12, model,  according to its lowest MSE in this study. Thus, this study may provide an integrated modelling approach as a decision-making supportive method for formulating price forecast of soybeans for the global soybean market.