From Traditional to Advanced Models: A Comparative Study between Time Series and Machine Leaning Models in Agriculture

Abstract

The agricultural sector plays a crucial role in the global economy, with edible oil crops like groundnut being vital commodities. Accurate price forecasting is essential for stakeholders, including farmers, traders, and policymakers. The primary aim of this study is to evaluate and compare the effectiveness of traditional time series models (such as ARIMA) and advanced deep learning models (such as RNN, GRU, and LSTM) in forecasting the monthly wholesale prices of groundnut. The analysis covers data from January 2014 to December 2023, collected from Agmarknet. Our results reveal that deep learning models, particularly LSTM, excel in capturing intricate patterns and delivering precise forecasts compared to traditional models. The LSTM model demonstrates superior performance, with RMSE, MAE, and MAPE values of 1.76, 1.02, and 0.25, respectively. This research enhances academic understanding of time series forecasting in agricultural economics and provides valuable insights for refining market predictions and improving decision-making processes.