Stacked Boost Forest: A Hybrid Model to Predict Domestic Cinnamon Purchasing Cost in Down South Sri Lanka
Himashi Raveena Liyanage
Department of Information and Communication Technology, University of Sri Jayewardenepura, Nugegoda, Sri Lanka.
Maheesha Dhashantha Silva
*
Department of Information and Communication Technology, University of Sri Jayewardenepura, Nugegoda, Sri Lanka.
*Author to whom correspondence should be addressed.
Abstract
Sri Lanka is the leading exporter of true cinnamon, providing 90% of global demand. However, domestic farmers face challenges in securing a stable market price due to varying prices set by different intermediate buyers and a lack of awareness of price fluctuation patterns. This research aims to develop a web-based forecasting system to predict the highest and average purchase prices of cinnamon from domestic farmers in southern Sri Lanka, using historical data from 2016 to 2024. The study introduces a hybrid model incorporating a Random Forest Regressor, a Gradient Boosting Regressor, and a Stacking Regressor with a Linear Regression meta-model, achieving 96% accuracy for the highest price prediction and 98% accuracy for average price prediction. Compared to previous studies that primarily focus on the export market, this research analyzes both external and internal factors influencing price fluctuations and considers both domestic and export markets. The proposed system provides stakeholders with a user-friendly platform to enhance price transparency and stability. Future work aims to expand the forecast coverage to the entire country and introduce a comparative report feature for year-over-year price analysis.
Keywords: Cinnamon, machine learning, linear regression, random forest, domestic price prediction