Advanced Machine Learning Techniques for Accurate Forecasting of Crude Palm Oil Price

The accurate forecasting of crude palm oil (CPO) prices is of paramount importance to stakeholders across the agricultural and financial sectors, as it directly influences critical decisions related to production, trading, and investment strategies. Traditional time series models, while valuable, often fall short in capturing the intricate, non-linear dynamics inherent in CPO price fluctuations. This research delves into the application of cutting-edge machine learning techniques, with a particular emphasis on state-of-the-art models like transformers and hybrid architectures, to significantly enhance the precision of CPO price predictions. This study provides a comprehensive overview of existing research on traditional CPO forecasting methodologies, while also exploring the promising potential of machine learning applications in this domain. By critically analyzing previous studies and highlighting emerging trends, this preliminary investigation aims to establish a benchmark for future research in the field of CPO price prediction. The findings presented herein are intended to serve as a valuable reference point, illuminating the progress made thus far and identifying key areas for further exploration.