Forecasting crude oil and natural gas spot prices by classification methods
AbstractIn 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.
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Bibliographic InfoPaper provided by Centro de Economía Aplicada, Universidad de Chile in its series Documentos de Trabajo with number 229.
Date of creation: 2006
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-04-21 (All new papers)
- NEP-CMP-2007-04-21 (Computational Economics)
- NEP-ECM-2007-04-21 (Econometrics)
- NEP-ENE-2007-04-21 (Energy Economics)
- NEP-FOR-2007-04-21 (Forecasting)
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