Machine Learning based Prediction of GDP using FAO Agricultural Data Set for Hungary

Abstract

Prediction of economical growth is a complex task which is essential for planning sustainable economy. The economy has a wide range of indicators that are monitored and recorded by governments and international organizations. Agriculture is one of the most essential factors to modern day economical sustainability. The Food and Agriculture Organization keeps essential agricultural data sets which consist of records on production of crops and other agricultural products whose production strongly relates to the Gross Domestic Product of many countries. Assuming that total crop production and agricultural economy growth are highly related, the production of crops and total value of income from agriculture can be learnt by machine learning models. As data is recorded along an axis of time, it can be interpreted as a time series of various factors. Recurrent Neural Network excel in learning time series and sequential data. This paper presents experimental results on training various recurrent neural networks for modeling the changes of Agricultural and Gross Domestic Products. The paper details the data transformation, model building and model validation steps. Our experimental results showed that the models could achieve 85% accuracy.