Comparison of ARIMA and SARIMA for Forecasting Crude Oil Prices

Vika Putri Ariyanti; Tristyanti Yusnitasari

doi:10.29207/resti.v7i2.4895

Vika Putri Ariyanti Gunadarma University
Tristyanti Yusnitasari Gunadarma University

DOI: https://doi.org/10.29207/resti.v7i2.4895

Keywords: ARIMA, SARIMA, forecasting, crude oil price

Abstract

Crude oil price fluctuations affect the business cycle due to affecting the ups and downs of the growth of the economy, which one of the indicators of the economic business cycle phenomenon. The importance of oil price prediction requires a model that can predict future oil prices quickly, easily, and accurately so that it can be used as a reference in determining future policies. Machine learning is an accurate method that can be used in predicting and makes it easier to predict because there is no need to program computers manually. ARIMA is a machine learning algorithm while ARIMA that uses a seasonal component is called SARIMA. Based on background, research purpose is modeling crude oil price forecasting by ARIMA and SARIMA. Forecasting is done on daily crude oil price data taken from Yahoo Finance from January 27, 2020 to January 25, 2023. The evaluation results show the RMSE value of ARIMA and SARIMA is 1.905. The forecast result of 7 days ahead with ARIMA is 86.230003 while SARIMA is 86.260002. The research results are expected to be helpful for policy makers to adopt policies and make the right decisions in the use of crude oil.

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