Forecasting energy spot prices

Abstract

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 forecasts based on encompassing tests and mean-squared prediction error (MSPE). We find that at short-term horizons (e.g., 2-4 days), ARIMA tends to outperform both ANN and SVM. However, at long-term horizons (e.g., 10-20 days), we find that in general ARIMA is encompassed by these two methods, and that linear combinations of ANN and SVM forecasts are more accurate than their corresponding individual forecasts. Based on MSPE calculations, we reach similar conclusions: the two classification methods under consideration outperform ARIMA at longer time horizons.