Crude palm oil price prediction using multilayer perceptron and long short-term memory

Abstract

Crude Palm Oil is a leading commodity from Indonesia. Accurate prediction of Crude Palm Oil prices is very important to ensure future prices and help decision making. Study on crude palm oil prices is needed to anticipate fluctuations. In this study, prediction model was made using Multilayer Perceptron and Long Short-Term Memory. The optimization methods in this study are Stochastic Gradient Descent, Root Mean Square Propagation, and Adaptive Moment Estimation. The best model is selected based on Mean Square Error. Based on the results, Long Short-Term Memory model with Adaptive Moment Estimation optimization method is more optimal than Long Short-Term Memory with Stochastic Gradient Descent and Long Short-Term Memory with Root Mean Square Propagation. The prediction results using Long Short-Term Memory with Adam optimization show that the predicted value is not different from the actual value and Mean Absolute Percentage Error is 2.11%. This model has high forecasting accuracy because Mean Absolute Percentage Error is less than 10%.