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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Vydáno v:	Cogent economics & finance Ročník 11; číslo 1; s. 1 - 25
Hlavní autoři:	Terregrossa, Salvatore Joseph, Şener, Uğur
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Abingdon Taylor & Francis 31.12.2023 Cogent Taylor & Francis Ltd Taylor & Francis Group
Témata:	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
On-line přístup:	Získat plný text
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