Forecasting inter-related energy product prices

Abstract

Five inter-related energy products are forecasted one month into the future using both linear and nonlinear techniques. Both spot prices and data derived from those prices are used as input data in the models. The models are tested by running data from the following year through them. Results show that, even though all products are highly correlated, the prediction results are asymmetric. In forecasts for crude oil, heating oil, gasoline and natural gas, the nonlinear forecasts were best, while for propane, the linear model gave the lowest error.

Keywords:

• neural network
• regression model
• inter-related products
• forecasting