Comparative Analysis of ARIMA and LSTM Models for Agricultural Product Price Forecasting

Abstract

The fluctuations in vegetable prices can have an impact on the economy. Machine learning can identify price trend changes. This study investigates the performance of ARIMA and LSTM models in predicting price trends for agricultural products, focusing on greens and lotus roots. The objective was to ascertain the superior model in terms of accurately reflecting market oscillations—a critical aspect for stakeholders in the agricultural sector. The investigation contrasted the ARIMA model's adeptness at detecting linear tendencies against the LSTM's capacity to decode intricate nonlinear dynamics. Empirical assessment employing metrics such as RMSE, MAE, and SMAPE disclosed a consistent supremacy of the ARIMA model over the LSTM in both datasets. This was particularly evident within the lotus root forecasts, where the discrepancy in error metrics for LSTM was remarkably pronounced. The outcomes indicate that notwithstanding the sophisticated structure of LSTM, ARIMA models maintain their status as robust and precise tools for agricultural time series forecasting. This is especially pertinent in contexts demanding computational efficiency and model interpretability. This investigation evaluates the efficacy of ARIMA and LSTM models in forecasting time-series data for agricultural commodities.