Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network

This paper aims to develop an artificial neural network[sbnd]based forecasting model employing a nonlinear focused time-delayed neural network (FTDNN) for energy commodity market forecasts. To validate the proposed model, crude oil and natural gas prices are used for the period 2007–2020, including the Covid-19 period. Empirical findings show that the FTDNN model outperforms existing baselines and artificial neural network[sbnd]based models in forecasting West Texas Intermediate and Brent crude oil prices and National Balancing Point and Henry Hub natural gas prices. As a result, we demonstrate the predictability of energy commodity prices during the volatile crisis period, which is attributed to the flexibility of the model parameters, implying that our study can facilitate a better understanding of the dynamics of commodity prices in the energy market.