Is Facebook PROPHET Superior than Hybrid ARIMA Model to Forecast Crude Oil Price? (Adakah PROPHET Facebook Lebih Hebat daripada Model ARIMA Hibrid untuk Meramalkan Harga Minyak Mentah?)

Mukhriz Izraf Azman, Aziz and Mohamad Hardyman, Barawi and Hazrul, Shahiri (2022) Is Facebook PROPHET Superior than Hybrid ARIMA Model to Forecast Crude Oil Price? (Adakah PROPHET Facebook Lebih Hebat daripada Model ARIMA Hibrid untuk Meramalkan Harga Minyak Mentah?). Sains Malaysiana, 51 (8). pp. 2633-2643. ISSN 2735-0118

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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, and geopolitics. 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 show that the ARIMAbased machine learning approach performs better as compared to the Prophet model for both one-week and one-month forecast ahead intervals.

Item Type: Article
Uncontrolled Keywords: ARIMA; crude oil price; forecasting; Prophet.
Subjects: H Social Sciences > H Social Sciences (General)
P Language and Literature > P Philology. Linguistics
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Faculties, Institutes, Centres > Faculty of Cognitive Sciences and Human Development
Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Gani
Date Deposited: 19 Dec 2022 03:22
Last Modified: 19 Dec 2022 03:22
URI: http://ir.unimas.my/id/eprint/40924

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