This article describes a study of forecasting methods performed for the corporate purchasing function, which required monthly forecasts of high-volume rubber-commodity prices as an aid to formulating its future purchasing strategy. Four mathematical forecasting procedures are applied to the same set of rubber-commodity price-index data. The forecasting techniques used are the Box-Jenkins time-series method, multiple linear regression analysis, and two new regression-based techniques, referred to as minimum relative error regression analysis and dynamic regression analysis. The rationale behind each method is briefly described. The forecast results generated by each algorithm are presented in graphic and numerical form. The accuracy of each method is evaluated by comparing forecasted versus actual values of the rubber-commodity price index. For this data, the new minimum relative error regression technique compares quite favorably with the powerful Box-Jenkins method, followed by standard multiple regression. The dynamic regression method is the least accurate of the four in this application. © 1979.
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