FORECASTING CRUDE OIL PRICE USING ARIMA AND FACEBOOK PROPHET WITHI MACHINE LEARNING

Mukhriz Izraf Azman, Aziz and Mohamad Hardyman, Barawi (2021) FORECASTING CRUDE OIL PRICE USING ARIMA AND FACEBOOK PROPHET WITHI MACHINE LEARNING. In: 7th Annual ECOFI Virtual Symposium (AES2021), 28 July 2021, Via Zoom.

[img] PDF
machine1.pdf

Download (326kB)
Official URL: https://www.aesuum.com/

Abstract

Oil price forecasting has received a great deal of attention from practitioners and researchers alike, but it remains a difficult topic because of its dependency on a variety of factors, including the economic cycle, international relations, geopolitics, and so on. Forecasting the price of oil is a difficult but gratifying task. Motivated by this issue, we present a robust model for accurate crude oil price forecasting using ARIMA and PROPHET models based on machine learning technique to produce a reliable weekly and monthly crude oil price predictions. We apply the Savitzky Golay smoothing filter to get a better denoising performance for our forecast models. For model evaluation, we apply cross validation with sliding windows on both models and compares the performances using RMSE and MAPE. The results shows that the ARIMA- based machine learning approach performs better as compared to the PROPHET model for both one-week and one-month forecast ahead intervals.

Item Type: Proceeding (Paper)
Uncontrolled Keywords: crude oil price, forecasting, Arima, Fbprophet, UNIMAS, University, Borneo, Malaysia, Sarawak, Kuching, Samarahan, IPTA, education, Universiti Malaysia Sarawak
Subjects: H Social Sciences > HB Economic Theory
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Barawi
Date Deposited: 28 Sep 2021 07:07
Last Modified: 28 Sep 2021 07:07
URI: http://ir.unimas.my/id/eprint/36217

Actions (For repository members only: login required)

View Item View Item