Author(s)

Gurudeo Anand Tularam¹, Tareq Saeed^1,2

¹Mathematics and Statistics, Griffith Sciences (ENV), Environmental Futures Research Institute (EFRI), Griffith University, Brisbane, Australia.
²Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, KSA.

Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.

Oil Price, Univariate Time Series, Exponential Smoothing, Holt-Winters, ARIMA Models, Model Selection Criteria

Tularam, G. and Saeed, T. (2016) Oil-Price Forecasting Based on Various Univariate Time-Series Models. American Journal of Operations Research, 6, 226-235. doi: 10.4236/ajor.2016.63023.