Employing a generalized reduced gradient algorithm method to form combinations of steel price forecasts generated separately by ARIMA-TF and ANN models

The research objective of the present study is the development of a model for increased accuracy of steel-price forecasts, which is of paramount importance for firms who use steel as an input and thus need to make informed decisions with regard to an optimal amount and type of hedge against unfavour...

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Veröffentlicht in:	Cogent economics & finance Jg. 11; H. 1; S. 1 - 25
Hauptverfasser:	Terregrossa, Salvatore Joseph, Şener, Uğur
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Abingdon Taylor & Francis 31.12.2023 Cogent Taylor & Francis Ltd Taylor & Francis Group
Schlagworte:	Algorithms ARIMA transfer function Artificial Neural Network (ANN) C53 Forecasting and Prediction Methods Coal combination forecasting Constrained weighted least squares (WLS) regression analysis Errors Forecasting Gradient Algorithm constrained optimization Hedges Industrial production Iron ores Neural networks Prices Simulation Methods steel price forecasts Time series
ISSN:	2332-2039, 2332-2039
Online-Zugang:	Volltext
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