Forecasting energy product prices

Mary E. Malliaris · Steven G. Malliaris

29 citations0/0 files storedInternational Joint Conference on Neural Networks (IJCNN)

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Forecasting Techniques and Applications Stock Market Forecasting Methods Energy Load and Power Forecasting

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 validated by running data from the following year. 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 nonlinear model had the largest average absolute error.

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References

2002What Can History Teach Us? A Retrospective Examination of Long-Term Energy Forecasts for the United States 2003Neural network forecasts of Canadian stock returns using accounting ratios 2003Price relationships in the petroleum market: an analysis of crude oil and refined product prices 2003Neural Networks in Business Forecasting

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