Predicting wheat production in pakistan by using an artificial neural network approach

Abstract

Forecasting of wheat production is of great importance for farmers and agriculture policy makers to improve production planning decisions. Numerous studies proved that traditional econometric techniques face significant challenges in out of sample predictability tests due to model uncertainty and parameter instability. Recent studies introduce several machine learning algorithms to improve time series prediction accuracy. The purpose of this study is to develop a precise wheat production model using artificial neural networks (ANN). A total of 71 years' wheat production data from 1948 to 2018 is divided into training data and test data. The model is trained by using 53 years' data and forecasts the future wheat production for the remaining 14 years. There are 16 indicators used as input variables for wheat production and top ten most important variables highlighted. The findings show, that the model captures much of the trend, and some of the undulations of the original series. The results reveal that the most important features in wheat production includes production prevailing trends, momentum and volatility.