Oil-Price Forecasting Based on Various Univariate Time-Series Models

Gurudeo Anand Tularam,Tareq Saeed

doi:10.4236/ajor.2016.63023

Abstract

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.

Highlights

Forecasting is the process of developing hypotheses about future events [1], and forecasting models that predict future events are used in numerous fields such as economics and science because they are useful tools in decision making
We focused on an in-sample period, which means that the time series between October 2011 and September 2015 served to generate the forecasts of the three models, whereas the time series between October 2015 and March 2016 served as out-of-sample data against which the accuracies of the forecasts were measured
To determine the quality of the parameter set used in this work, we compared the forecast values obtained from the three above-mentioned models with the actual West Texas Intermediate (WTI) crude oil prices from October 2015 and March 2016

Summary

Introduction

Forecasting is the process of developing hypotheses about future events [1], and forecasting models that predict future events are used in numerous fields such as economics and science because they are useful tools in decision making. A perfect forecast provides insight into the implications of an action or inaction and serves as a metric to judge one’s ability to influence future events [2] [3]. The task of forecasting or modelling has conven-. How to cite this paper: Tularam, G.A. and Saeed, T. (2016) Oil-Price Forecasting Based on Various Univariate Time-Series Models. Saeed tionally been performed either by developing a model or by implementing techniques developed to assess time series [4]. Different models have been applied to forecast data over particular periods [5] [6]

Methods

Results

Conclusion