Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Indonesian Crude Oil Price

Authors

  • Masna Wati Universitas Mulawarman
  • Haviluddin Haviluddin Universitas Mulawarman
  • Akhmad Masyudi Universitas Mulawarman
  • Anindita Septiarini Universitas Mulawarman
  • Heliza Rahmania Hatta Universitas Mulawarman

DOI:

https://doi.org/10.26555/jiteki.v9i3.22286

Keywords:

ARIMA, ICP, Crude oil, Forecasting

Abstract

Crude oil is the main commodity of the global economy because oil is used as an ingredient for many industries globally and is the price base used in the state budget. Indonesian Crude Price (ICP) fluctuates following developments in world crude oil prices. A significant increase in crude oil prices will certainly disrupt the economy. Thus, the movement or fluctuation of ICP is essential for business players in the energy market, especially domestically. Therefore, crude oil price forecasting is needed to assist business people in making decisions related to the energy market. This study aims to find a suitable forecasting model for Indonesian crude oil prices using the Autoregressive Integrated Moving Average (ARIMA) method. The forecasting process used ICP time-series data per month for 50 types of crude oil within five years or 63 months. Based on the experimental results, it was found that the most fit ARIMA models were (0,1,1), (1,1,0), (0,1,0), and (1,2,1). The test results for April to September 2020 have a good and proper interpretation, except the type of BRC oil indicates inaccurate forecasts. The ARIMA error rate is very dependent on the value of the data before it is predicted and external factors, the more unstable the data value every month, the higher the error rate.

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Published

2023-07-31

How to Cite

[1]
M. Wati, H. Haviluddin, A. Masyudi, A. Septiarini, and H. R. Hatta, “Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Indonesian Crude Oil Price”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 9, no. 3, pp. 720–730, Jul. 2023.

Issue

Vol. 9 No. 3 (2023): September

Section

Articles

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