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

This paper aims to develop an artificial neural networkbased 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 networkbased 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. [Display omitted] •A nonlinear focused time-delayed neural network to forecast energy commodity prices.•Forecasting the daily prices of crude oil and natural gas for the period 2007–2020.•Nonlinear, dynamic, and non-stationary properties of time series.•Outperforms existing neural networkbased models in terms of forecast accuracy.•Predictability of energy commodity prices during the Covid-19 crisis.