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Sains Malaysiana 51(8)(2022):
http://doi.org/10.17576/jsm-2022-5108-22
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 AZIZ1, MOHAMAD HARDYMAN BARAWI2 & HAZRUL
SHAHIRI3,*
1School
of Economic, Finance and Banking (SEFB), Universiti Utara Malaysia, 06010 Sintok, Kedah Darul Aman, Malaysia
2Faculty
of Cognitive Sciences and Human Development, University Malaysia Sarawak, 94300
Kota Samarahan, Sarawak, Malaysia
3Center
for Sustainable and Inclusive Development, Faculty of Economics and Management,
Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia
Received: 16 August
2021/Accepted: 30 January 2022
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 ARIMA- based machine learning approach performs better as compared to the
Prophet model for both one-week and one-month forecast ahead intervals.
Keywords: ARIMA; crude oil price; forecasting;
Prophet
Abstrak
Ramalan harga minyak telah mendapat banyak perhatian daripada pengamal dan penyelidik, tetapi ia kekal sebagai topik yang sukar kerana pergantungannya pada pelbagai faktor, termasuk kitaran ekonomi, hubungan antarabangsa dan geopolitik. Meramalkan harga minyak adalah tugas yang sukar tetapi menggembirakan. Didorong oleh isu ini, kami mempersembahkan model teguh untuk ramalan harga minyak mentah yang tepat menggunakan model ARIMA dan Prophet berdasarkan teknik pembelajaran mesin untuk menghasilkan ramalan harga minyak mentah mingguan dan bulanan yang boleh dipercayai. Kami menggunakan penapis pelicinan Savitzky–Golay untuk mendapatkan prestasi nyahbunyi yang lebih baik untuk model ramalan kami. Untuk penilaian model, kami menggunakan pengesahan silang dengan tingkap gelongsor pada kedua-dua model
dan membandingkan prestasi menggunakan RMSE dan MAPE. Keputusan menunjukkan bahawa pendekatan pembelajaran mesin berasaskan ARIMA menunjukkan prestasi yang lebih baik berbanding model Prophet untuk kedua-dua ramalan satu minggu dan satu bulan ke hadapan.
Kata kunci: ARIMA; harga minyak mentah; ramalan; Prophet
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*Corresponding
author; email: hizuan@ukm.edu.my
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