Palm Oil Price Forecasting in Malaysia Using 2 Satisfiability Based Reverse Analysis Method via Radial Basis Function Neural Network

Abstract

This paper aims to forecast the performance of palm oil price (POP) in Malaysia by using
artificial neural networks (ANN), namely radial basis function neural network (RBFNN). Monthly time
series data spanning from Jan. 31, 2016 to March 31, 2020, of Malaysian monthly price for palm oil are
used. Computer simulations are carried out to demonstrate and verify the ability of radial basis function
neural network for forecasting the Malaysian monthly palm oil price. The results obtained proved the
durability of the RBFNN network in forecasting the Malaysian monthly palm oil price. The results also
indicate that the price of palm oil is highly influenced by Total Exports (MT) of Palm Oil, Total Imports
(MT) of Palm Oil, and Production of Palm Oil. This study enables the Malaysian palm oil industry to
continue dominating the international market