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A study of univariate forecasting methods for crude oil price

A study of univariate forecasting methods for crude oil price This paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.Design/methodology/approachSix different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.FindingsThe authors found that the grey forecast is a reliable forecasting method for crude oil prices.Originality/valueThe contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Maritime Business Review Emerald Publishing

A study of univariate forecasting methods for crude oil price

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Publisher
Emerald Publishing
Copyright
© Pacific Star Group Education Foundation
ISSN
2397-3757
DOI
10.1108/mabr-09-2021-0076
Publisher site
See Article on Publisher Site

Abstract

This paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.Design/methodology/approachSix different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.FindingsThe authors found that the grey forecast is a reliable forecasting method for crude oil prices.Originality/valueThe contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

Journal

Maritime Business Review – Emerald Publishing

Published: Mar 7, 2023

Keywords: Forecasting accuracy comparison; Univariate forecasting models; Crude oil price

References

  • Modeling and forecasting Oman crude oil prices using Box-Jenkins techniques
    Ahmad, M.I.
  • Crude oil price forecasting techniques: a comprehensive review of literature
    Bashiri Behmiri, N.; Pires Manso, J.R.
  • Introduction grey system theory
    Deng, J.L.
  • Forecasting crude oil consumption in China using a grey prediction model with an optimal fractional-order accumulating operator
    Duan, H.; Lei, G.R.; Shao, K.
  • Crude oil price prediction using Artificial Neural Network
    Gupta, N.; Nigam, S.
  • Analysis of global oil consumption based on grey prediction model
    Huang, S.P.; Zhang, L.; Wang, M.H.; Wang, R.; University, C.J.
  • Prediction of international crude oil futures price based on GM(1,1)
    Lin, A.
  • Research on international crude oil price forecasting model
    Lyu, H.; Chang, Y.
  • The accuracy of extrapolation (time series) methods: results of a forecasting competition
    Makridakis, S.; Anderson, A.; Carbone, R.; Fildes, R.; Hibon, M.; Lewandowski, R.; Newton, J.; Parzen, E.; Winkler, R.
  • Black gold falls, black plague arise -an OPEC crude oil price forecast using a gray prediction model
    Norouzi, N.; Fani, M.
  • Forecasting OPEC oil price: a comparison of parametric stochastic models
    Nwafor, C.N.; Oyedele, A.A.
  • A comparison of univariate methods for forecasting container throughput volumes
    Peng, W.Y.; Chu, C.W.
  • Modeling and forecasting petroleum futures volatility
    Sadorsky, P.
  • Forecasting crude oil price using ARIMA models
    Selvi, J.J.; Shree, R.K.; Krishnan, J.
  • Application of ARIMA model in short-term prediction of international crude oil price
    Xiang, Y.; Zhuang, X.H.
  • Research on China's oil consumption demand forecast based on Grey System Theory
    Xu, R.S.
  • A comparative study on Box-Jenkins and Garch models in forecasting crude oil prices
    Yaziz, S.R.; Ahmad, M.H.; Nian, L.C.; Muhammad, N.
  • A monthly crude oil spot price forecasting model using relative inventories
    Ye, M.T.; Zyren, J.; Shore, J.
  • Beyond accuracy: comparison of criteria used to select forecasting methods
    Yokum, J.T.; Armstrong, J.S.
  • Forecasting crude oil price with an autoregressive integrated moving average (ARIMA) model
    Zhao, C.; Wang, B.
  • Modeling and forecasting monthly crude prices of Pakistan: a comparative study of ARIMA, GARCH and ARIMA−GARCH Models
    Aamir, M.; Shabri, A.B.
  • Crude oil prices forecasting: time series vs. SVR models
    He, X.J.
  • Assessing linear and nonlinear models to forecast OPEC oil prices
    Safari, A.; Mohammadi, M.
  • Oil-price forecasting based on various univariate time-series models
    Tularam, G.A.; Saeed, T.
  • Forecasting the price of fuel oil: a STL−(ELM+ARIMA) combination approach
    Yu, F.; Liu, Y.; Zhang, C.