Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production

Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production

This paper applies a heuristic approach to optimize the predictor variables in artificial neural networks when forecasting raw material prices for energy production (coking coal, natural gas, crude oil and coal) to achieve a better forecast. Two goals are (1) to determine the optimum number of time-...

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Bibliographic Details
Main Authors: Marta Matyjaszek, Gregorio Fidalgo Valverde, Alicja Krzemień, Krzysztof Wodarski, Pedro Riesgo Fernández
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Energies
Subjects:
raw material
price forecasting
artificial neural network
predictor variable
lagged variable size
rolling window
Online Access:https://www.mdpi.com/1996-1073/13/8/2017

Internet

https://www.mdpi.com/1996-1073/13/8/2017

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