Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

Abstract

Abstract. The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps

“…An oil palm prediction model to estimate production from cultivated area images and tree age estimation is proposed in [59]. Forthcoming CPO prices are predicted in [60,61] based on historical trends in CPO prices. The performances of two regression models trained from historical data of oil palm production are compared to predict future production in [62].…”

A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

“…Although MLP receives one-dimensional data, we expected MLP to learn data patterns because units of hidden layers are all connected to input nodes. There are also studies where MLP learned time-series data [48,49]. Before inputting data to MLP, because the length of the target signal in a pattern may be different for each pattern, it is necessary to match the length of the signal and then input it into the network.…”

Optimization of Technical Analysis in the Palm Oil Marketing Strategy: Daily Pricing Model for 30 Days Transactions

“…Hasil-hasil penelitian terkait peramalan harga minyak kelapa sawit berbasis analisis univariate maupun multivariate time series hingga model ekonometrika relatif telah banyak dilakukan. Beberapa penelitian terkait, antara lain penelitian Kanchymalay et al (2017), Ariff et al (2015), Ahmad et al (2014), Ahmed dan Shabri (2014), Kantaporn et al (2013), Khin et al (2013), Karia (2013), Karia dan Bujang (2011), dan Kurniawan (2011). Data basis yang menjadi unit ramalan pada analisis univariate maupun multivariate time series pada umumnya adalah harga bulanan dan sedikit yang berbasis data harian serta berupa peramalan post-ante untuk sebuah periode tertentu.…”