Fuzzy time series forecasting of bunker prices: Nonstationary considerations

This work explores the applicability of well-known fuzzy time series forecasting techniques for the prediction of bunker prices. These techniques have extensively been used with great success to the forecasting of stock prices. In the present work, weekly time series of bunker prices in four major world ports (Rotterdam, Houston, Singapore, and Fujairah) have been thoroughly examined and used for the verification of the forecasting performance of the fuzzy models. The following bunker types have been examined: 380cSt (high and low sulphur), 180cSt (high sulphur), marine diesel oil (MDO), and marine gas oil (MGO). To examine the forecasting accuracy, four error measures are used as an evaluation criterion to compare the forecasting performance of the listing models. Before applying the fuzzy forecasting procedure, and in order to remove nonstationarity, both differencing and moving-average are applied to the data. It has been found that all four error measures attain their minimum at the same point M opt , which in turn gives the closer forecasts to the actual values. As the importance of fuel prices increases, effective forecasting could further benefit operators with compliance issues and financial planning as well as regulators estimating better the timing and the cost of regulation.