Enhancing Cardamom Price Forecasting: Integration of XG Boost Model for a Robust Ensemble Model

This study focuses on predicting future cardamom prices in Kerala using data from the Spices Board of Kerala (2014–2024). We propose an ensemble model that integrates the XG Boost machine learning algorithm to enhance predictive accuracy. Our analysis identified daily average price and date as sufficient predictors for forecasting cardamom prices. The results demonstrate that the hybrid ensemble model, particularly with XG Boost, outperforms traditional forecasting methods. These findings highlight the effectiveness of tailored machine learning approaches for complex agricultural markets and suggest a generally stable price structure for cardamom in Kerala, underlining the importance of strategic planning to support farmers' livelihoods.