Analyzing time series to forecast hot rolled coil steel price in Spain by means of neural non-linear models-Reference-Cited by-同舟云学术

Analyzing time series to forecast hot rolled coil steel price in Spain by means of neural non-linear models

Published:2024-05-20 Issue: Volume: Page:
ISSN:1367-0751
Container-title:Logic Journal of the IGPL
language:en
Short-container-title:

Author:

Alcalde Roberto¹²,GarcÍa Santiago³⁴,Manzanedo Manuel⁵⁶,Basurto Nuño⁷⁸,de Armiño Carlos Alonso⁹¹⁰,Urda Daniel¹¹¹²,Alonso Belén¹³¹⁴

Affiliation:

1. Departamento de Economía y Administración de Empresas , Facultad de Ciencias Económicas y Empresariales, , S/N, 09001 Burgos , Spain , radelgado@ubu.es

2. Universidad de Burgos, Pza. de la Infanta Dña. Elena , Facultad de Ciencias Económicas y Empresariales, , S/N, 09001 Burgos , Spain , radelgado@ubu.es

3. Departamento de Ingeniería de Organización , Escuela Politécnica Superior, , Av. Cantabria s/n, 09006, Burgos , Spain , lgpineda@ubu.es

4. Universidad de Burgos , Escuela Politécnica Superior, , Av. Cantabria s/n, 09006, Burgos , Spain , lgpineda@ubu.es

5. Departamento de Digitalización , Escuela Politécnica Superior, , Av. Cantabria s/n, 09006, Burgos , Spain , mms0133@alu.ubu.es

6. Universidad de Burgos , Escuela Politécnica Superior, , Av. Cantabria s/n, 09006, Burgos , Spain , mms0133@alu.ubu.es

7. Departamento de Digitalización , Escuela Politécnica Superior, , Av. Cantabria s/n, 09006, Burgos , Spain , nbasurto@ubu.es

8. Universidad de Burgos , Escuela Politécnica Superior, , Av. Cantabria s/n, 09006, Burgos , Spain , nbasurto@ubu.es

9. Departamento de Digitalización , Escuela Politécnica Superior, , Av. Cantabria s/n, 09006, Burgos , Spain , caap@ubu.es

10. Universidad de Burgos , Escuela Politécnica Superior, , Av. Cantabria s/n, 09006, Burgos , Spain , caap@ubu.es

11. Departamento de Digitalización , Escuela Politécnica Superior, , Av. Cantabria s/n, 09006, Burgos , Spain , durda@ubu.es

12. Universidad de Burgos , Escuela Politécnica Superior, , Av. Cantabria s/n, 09006, Burgos , Spain , durda@ubu.es

13. Departamento de Química , Escuela Politécnica Superior, , C/Villadiego s/n. 09001 Burgos , balonso@ubu.es

14. Universidad de Burgos , Escuela Politécnica Superior, , C/Villadiego s/n. 09001 Burgos , balonso@ubu.es

Abstract

Abstract In the industrial context, steel is a broadly-used raw material with applications in many different fields. Due to its high impact in the activity of many industries all over the world, forecasting its price is of utmost importance for a huge amount of companies. In this work, non-linear neural models are applied for the first time to different datasets in order to validate their suitability when predicting the price of this commodity. In particular, the NAR, NIO and NARX neural network models are innovatively applied for the first time to forecast the price of hot rolled steel in Spain. Besides these variety of models, different datasets consisting of a set of heterogenous variables from the last seven years and related to the price of this commodity are benchmarked and analyzed. The results showed that NARX is the best performing model when the price of raw materials used to produce steel and the stock market prices of three major global steel producing companies are employed as input to this predictive model. Consequently, this result may boost the application of Machine Learning in companies, in order to schedule the supplying operations according to the price forecasting.

Publisher

Oxford University Press (OUP)

Link

https://academic.oup.com/jigpal/advance-article-pdf/doi/10.1093/jigpal/jzae060/57745615/jzae060.pdf

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