Forecasting Thai Agricultural Price: A Deep Learning Approach for Key Commodities

Accurate price forecasting is essential for informed decision-making by stakeholders in the agricultural sector, including farmers, traders, and policymakers.This study explored the use of deep learning models to predict market prices of four major agricultural commodities in Thailand, including rice, corn, cassava, and sugarcane.We evaluated and compared three network architectures across multiple forecasting horizons on a dataset from the Thai market.Overall, our findings suggested that the Long-and Short-term Time-series network or LSTNet was the most stable model, highlighting the advantage of capturing relationships among multiple variables.The results essentially pinpointed the strengths and limitations of each model, emphasizing the need for careful model selection based on characteristics of individual agricultural products.