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Comparison of the accuracy of Box-Jenkins and Holt-Winters methods for forecasting OPEC crude oil price | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 28، دوره 16، شماره 4، تیر 2025، صفحه 345-359 اصل مقاله (763.47 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2024.32531.4839 | ||
| نویسندگان | ||
| Kobra Hosseini1؛ Mohammad Ali Keramati* 1؛ Mohammad Ali Afshar Kazemi1؛ Zadollah Fathi2 | ||
| 1Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| 2Department of Management, Center Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| تاریخ دریافت: 12 آبان 1402، تاریخ پذیرش: 10 بهمن 1402 | ||
| چکیده | ||
| Various methods have recently been proposed to predict essential economic and non-economic variables. Each forecasting method has its own advantages and disadvantages based on the nature of the input data. Box-Jenkins and Halt-Winters methods are among the new approaches for increasing the accuracy of forecasting results. Therefore, this research aimed to predict OPEC average oil price data for June 2022 to May 2024 based on the data from 2003 to 2022 using Box-Jenkins and Holt-Winters methodology with a single variable. The errors of Box-Jenkins methodology, among the time series, processes ARIMA 5.1.5, ARIMA 4.1.5, ARIMA 3.1.5, and ARIMA 5.1.3 have the best accuracy with MSE of 61.86, 63.21, 63.29, and 63.62, respectively. The accuracy of the Holt-Winters method was not appropriately compared to the time series method due to the nature of the data. | ||
| کلیدواژه‌ها | ||
| forecasting؛ time series؛ Box-Jenkins؛ Holt-Winters | ||
| مراجع | ||
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[1] M. Abd Elaziz, A.A. Ewee, and Z. Alameer, Improving adaptive neuro-fuzzy inference system based on a modified salp swarm algorithm using genetic algorithm to forecast crude oil price, Natural Resources Res. 29 (2020), 2671–2686. [2] H. Abdollahi, A novel hybrid model for forecasting crude oil price based on time series decomposition, Appl. Energy 267 (2020), 115035. [3] L. Chen, Z. Zhang, F. Chen, and N. Zhou, A study on the relationship between economic growth and energy consumption under the new normal, Nat. Account. Rev. 1 (2019), no. 1, 28–41. [4] D. Fan, H. Sun, J. Yao, K. Zhang, X. Yan, and Z. Sun, Well production forecasting based on ARIMA-LSTM model considering manual operations, Energy 220 (2021), 119708. [5] M. Friedman, The methodology of positive economics, D.M. Hausman (Eds.), The philosophy of economics: An anthology, Cambridge University Press, 1994. [6] A.C. Harvey, The Econometric Analysis of Time Series, MIT Press, 1990. [7] M. Istan, N. Husainah, M. Murniyanto, A. Suganda, I. Siswanti, and M. Fahlevi, The effects of production and operational costs, capital structure and company growth on the profitability: Evidence from manufacturing industry, Account. 7 (2021), no. 7, 1725-1730. [8] I. Karakurt, Modelling and forecasting the oil consumptions of the BRICS-T countries, Energy 220 (2021), 119720. [9] T. Li and G. Liao, The heterogeneous impact of financial development on green total factor productivity, Front. Energy Res. 8 (2020), 29. [10] Y. Lyu, S. Tuo, Y. Wei, and M. Yang, Time-varying effects of global economic policy uncertainty shocks on crude oil price volatility: New evidence, Resources Policy 70 (2021), 101943. [11] A. Murat and E. Tokat, Forecasting oil price movements with crack spread futures, Energy Econ. 31 (2009), no. 1, 85–90. [12] N. Omidi and M.R. Omidi, Estimating accident-related traumatic injury rate by future studies models in Semnan province, Iran, Health Emergen. Disasters Quart. 3 (2021), no. 4, 191–198. [13] P. Saculsan and T. Kanamura, Examining risk and return profiles of renewable energy investment in developing countries: The case of the Philippines, Munich Personal RePEc Arch. (2019), no. 97473. https://mpra.ub.unimuenchen.de/id/eprint/97473 [14] S. Smiech, M. Papiez, M. Rubaszek and M. Snarska, The role of oil price uncertainty shocks on oil-exporting countries, Energy Econ. 93 (2021), 105028. [15] L. Yu, R. Zha, D. Stafylas, K. He and J. Liu, Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models, Int. Rev. Financ. Anal. 68 (2020), 101280. [16] Y. Zhang, F. Ma and Y. Wang, Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?, J. Empir. Financ. 54 (2019), 97–117. [17] Y. Zhao, J. Li and L. Yu, A deep learning ensemble approach for crude oil price forecasting, Energy Econ. 66 (2017), 9–16. [18] Y. Zheng and Z. Du, A systematic review in crude oil markets: Embarking on the oil price, Green Finance 1 (2019), no. 3, 328–345. | ||
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