WTI Crude Oil Price Forecasting for the Next Year Using ARIMA Model
DOI:
https://doi.org/10.54097/c70fak89Keywords:
WTI crude oil; ARIMA model; forecasting.Abstract
The analysis and prediction of crude oil prices carry significant importance due to the increasing prominence of crude oil in the global economic market and its impact on individuals' daily lives. The crude oil price has significance in other industries, and there is an increasing presence of derivatives such as stocks and options. An escalation in the price of crude oil may result in an upward trend in gasoline prices, augmented shipping expenses, and amplified input costs for companies. This study utilizes the WTI crude oil price data from the first day of each month from January 2019 to September 2023 to provide a projection for the WTI crude oil price in 2024. During the investigation, identifying a trend demonstrating normalcy within the dataset has been utilized to effectively incorporate the ARIMA model, thereby serving the objective of forecasting. The phenomenon of bounce between up and down experiences a transition towards a flattened pattern by September 2023. Based on our forecasting analysis, it has been determined that the crude oil price is projected to exhibit a sustained plateau in 2024. While minor fluctuations in price may occur, the overall trend indicates a steady value of approximately 100 USD/Bbl.
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Hong Miao, Sanjay Ramchander, Tianyang Wang, et al. Influential factors in crude oil price forecasting. Energy Economics, 2017, 68: 77-88.
Vesa Soini, Sindre Lorentzen. Option prices and implied volatility in the crude oil market, Energy Economics, 2019, 83: 515-539.
Farzana Alamgir, Sakib Bin Amin, The nexus between oil price and stock market: Evidence from South Asia, Energy Economics, 2021, 7: 693-703.
James, D. Hamilton. Oil and the macroeconomy since world war II. Journal of Political Economy, 1983, 91: 228-248.
Kumar Biswajit Debnath, Monjur Mourshed. Forecasting methods in energy planning models. Renewable and Sustainable Energy Reviews, 2018, 88: 297-325.
Kanishka Tyagi, Chinmay Rane, Harshvardhan, et al. Artificial Intelligence and Machine Learning. EDGE Computing, 2022, 53-63.
Sungil Kim, Heeyoung Kim. A new metric of absolute percentage error for intermittent demand forecasts. 2016, 32: 669-679.
George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, et al. Time Series Analysis: Forecasting and Control. John Wiley & Sons, Inc., 2016, 7.
Rob J Hyndman, George Athanasopoulos. Forecasting: principles and practice. OTEXTS, 2018, 3.
Andreea-Cristina Petrică, Stelian Stancu, Alexandru Tindeche. Limitation of ARIMA models in financial and monetary economics. Theoretical and Applied Economics, 2016, XXIII.4: 19-42.
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