Deep long short-term memory based model for agricultural price forecasting

Agricultural price forecasting is one of the research hotspots in time series forecasting due to its unique characteristics. In this paper, we developed a deep long short-term memory (DLSTM) based model for the accurate forecasting of a nonstationary and nonlinear agricultural prices series. DLSTM model is a type of deep neural network which is advantageous in capturing the nonlinear and volatile patterns by utilizing both the recurrent architecture and deep learning methodologies together. The study further compares the price forecasting ability of the developed DLSTM model with conventional time-delay neural network (TDNN) and ARIMA models using international monthly price series of maize and palm oil. The empirical results demonstrate the superiority of the developed DLSTM model over other models in terms of various forecasting evaluation criteria like root mean square error, mean absolute percentage error and mean absolute deviation. The DLSTM model also showed dominance over other models in predicting the directional change of those monthly price series. Moreover, the accuracy of the forecasts obtained by all the models is also evaluated using the Diebold-Mariano test and the Friedman test whose results validate that the DLSTM based model has a clear advantage over the other two models.