Forecasting crude oil and natural gas spot prices by classification methods
In this article, we forecast crude oil and natural gas spot prices at a daily frequency based on two classification techniques: artificial neural networks (ANN) and support vector machines (SVM). As a benchmark, we utilize an autoregressive integrated moving average (ARIMA) specification. We evaluate out-of-sample forecast based on encompassing tests and mean-squared prediction error (MSPE). We find that at short-time horizons (e.g., 2-4 days), ARIMA tends to outperform both ANN and SVM. However, at longer-time horizons (e.g., 10-20 days), we find that in general ARIMA is encompassed by these two methods, and linear combinations of ANN and SVM forecasts are more accurate than the corresponding individual forecasts. Based on MSPE calculations, we reach similar conclusions: the two classification methods under consideration outperform ARIMA at longer time horizons.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Clements,Michael & Hendry,David, 1998.
"Forecasting Economic Time Series,"
Cambridge University Press, number 9780521634809, Junio.
- Francis X. Diebold, 1997.
"The Past, Present, and Future of Macroeconomic Forecasting,"
NBER Working Papers
6290, National Bureau of Economic Research, Inc.
- Francis X. Diebold, 1998. "The Past, Present, and Future of Macroeconomic Forecasting," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 175-192, Spring.
- Francis X. Diebold, 1997. "The past, present, and future of macroeconomic forecasting," Working Papers 97-20, Federal Reserve Bank of Philadelphia.
- Diebold, Francis X & Mariano, Roberto S, 1995.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 13(3), pages 253-63, July.
- Tom Doan, . "DMARIANO: RATS procedure to compute Diebold-Mariano Forecast Comparison Test," Statistical Software Components RTS00055, Boston College Department of Economics.
- Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Fang, Yue, 2003. "Forecasting combination and encompassing tests," International Journal of Forecasting, Elsevier, vol. 19(1), pages 87-94.
- Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
- Lanza, Alessandro & Manera, Matteo & Giovannini, Massimo, 2005. "Modeling and forecasting cointegrated relationships among heavy oil and product prices," Energy Economics, Elsevier, vol. 27(6), pages 831-848, November.
- Dooley, Gillian & Lenihan, Helena, 2005. "An assessment of time series methods in metal price forecasting," Resources Policy, Elsevier, vol. 30(3), pages 208-217, September.
- Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
When requesting a correction, please mention this item's handle: RePEc:edj:ceauch:229. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.