Application of Univariate and Multivariate Long Short Term Memory for World Crude Palm Oil Price Prediction Penerapan Long Short Term Memory Peubah Tunggal dan Ganda untuk Prediksi Harga Minyak Kelapa Sawit Dunia
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Abstract
Time series analysis is essential for predicting economic and other important factors; it can be done univariately or multivariately. Technological developments created long short term memory that can handle vanishing gradients and long-term dependencies. This research will predict the world price of crude palm oil because Indonesia, as the world's largest crude palm oil producer, is strongly influenced by the world crude palm oil price. This study uses monthly data on crude palm oil, soybean oil, and crude oil prices from January 2002 to May 2024 obtained from the World Bank Commodity Price Data. This research applies univariate and multivariate long short term memory to predicting crude palm oil prices. The use of long short term memory is because the data shows non-linear elements and high volatility. The input used for univariate long short term memory is the crude palm oil price, while multivariate long short term memory uses crude palm oil, soybean oil, and crude oil prices. The univariate long short term memory proved to be more effective in the case of world crude palm oil price prediction. This is proven by the lower mean absolute percentage error of 6,574% compared to the multivariate long short term memory of 6,689%. This univariate long short term memory uses a combination of hyperparameters: neuron 32, epoch 100, time steps 1, batch size 64, and learning rate 0,01.
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