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- W4313442674 endingPage "101863" @default.
- W4313442674 startingPage "101863" @default.
- W4313442674 abstract "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 to 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." @default.
- W4313442674 created "2023-01-06" @default.
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- W4313442674 date "2023-01-01" @default.
- W4313442674 modified "2023-10-12" @default.
- W4313442674 title "Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network" @default.
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- W4313442674 doi "https://doi.org/10.1016/j.ribaf.2022.101863" @default.
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