Forecasting Global Palm Oil Prices Using Fuzzy Time Series Markov Chain Approach - Usep Rahmat; Yenni Sutedjo (International Journal of Quantitative Research and Modeling, 2025)
Global palm oil prices exhibit a fluctuating pattern, characterized by both upward and downward movements that are influenced by changes in stock levels as well as global demand. These factors are inherently difficult to predict with precision, creating challenges for palm oil entrepreneurs in formulating effective business strategies. To design appropriate strategies, entrepreneurs require comprehensive information on global palm oil prices, including historical, current, and projected data. Moreover, the level of forecasting accuracy is an essential consideration to ensure that the strategies developed are both reliable and effective. This study aims to forecast global palm oil prices using the Fuzzy Time Series–Markov Chain method and to evaluate the predictive accuracy of the resulting price estimates. The dataset used in this research consists of secondary data, namely global palm oil price records spanning the period from December 19, 2024, to March 13, 2025, comprising 50 observations obtained from the id.investing website. The analysis produced forecasted global palm oil prices for the subsequent three-day period, namely March 17-19, 2025, with predicted values of 4533.25; 4513.82; and 4530.37 (in MYR), respectively. The model achieved a Mean Absolute Percentage Error (MAPE) of 0.81%, corresponding to a forecasting accuracy rate of 99.19%.