{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T01:17:45Z","timestamp":1772068665834,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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.<\/jats:p>","DOI":"10.1093\/jigpal\/jzae060","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T13:04:25Z","timestamp":1711631065000},"source":"Crossref","is-referenced-by-count":1,"title":["Analyzing time series to forecast hot rolled coil steel price in Spain by means of neural non-linear models"],"prefix":"10.1093","volume":"33","author":[{"given":"Roberto","family":"Alcalde","sequence":"first","affiliation":[{"name":"Departamento de Econom\u00eda y Administraci\u00f3n de Empresas , Facultad de Ciencias Econ\u00f3micas y Empresariales, , S\/N, 09001 Burgos , , radelgado@ubu.es","place":["Spain"]},{"name":"Universidad de Burgos, Pza. de la Infanta D\u00f1a. Elena , Facultad de Ciencias Econ\u00f3micas y Empresariales, , S\/N, 09001 Burgos , , radelgado@ubu.es","place":["Spain"]}]},{"given":"Santiago","family":"Garc\u00cda","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda de Organizaci\u00f3n , Escuela Polit\u00e9cnica Superior, , Av. Cantabria s\/n, 09006, Burgos , , lgpineda@ubu.es","place":["Spain"]},{"name":"Universidad de Burgos , Escuela Polit\u00e9cnica Superior, , Av. Cantabria s\/n, 09006, Burgos , , lgpineda@ubu.es","place":["Spain"]}]},{"given":"Manuel","family":"Manzanedo","sequence":"additional","affiliation":[{"name":"Departamento de Digitalizaci\u00f3n , Escuela Polit\u00e9cnica Superior, , Av. Cantabria s\/n, 09006, Burgos , , mms0133@alu.ubu.es","place":["Spain"]},{"name":"Universidad de Burgos , Escuela Polit\u00e9cnica Superior, , Av. Cantabria s\/n, 09006, Burgos , , mms0133@alu.ubu.es","place":["Spain"]}]},{"given":"Nu\u00f1o","family":"Basurto","sequence":"additional","affiliation":[{"name":"Departamento de Digitalizaci\u00f3n , Escuela Polit\u00e9cnica Superior, , Av. Cantabria s\/n, 09006, Burgos , , nbasurto@ubu.es","place":["Spain"]},{"name":"Universidad de Burgos , Escuela Polit\u00e9cnica Superior, , Av. Cantabria s\/n, 09006, Burgos , , nbasurto@ubu.es","place":["Spain"]}]},{"given":"Carlos Alonso","family":"de Armi\u00f1o","sequence":"additional","affiliation":[{"name":"Departamento de Digitalizaci\u00f3n , Escuela Polit\u00e9cnica Superior, , Av. Cantabria s\/n, 09006, Burgos , , caap@ubu.es","place":["Spain"]},{"name":"Universidad de Burgos , Escuela Polit\u00e9cnica Superior, , Av. Cantabria s\/n, 09006, Burgos , , caap@ubu.es","place":["Spain"]}]},{"given":"Daniel","family":"Urda","sequence":"additional","affiliation":[{"name":"Departamento de Digitalizaci\u00f3n , Escuela Polit\u00e9cnica Superior, , Av. Cantabria s\/n, 09006, Burgos , , durda@ubu.es","place":["Spain"]},{"name":"Universidad de Burgos , Escuela Polit\u00e9cnica Superior, , Av. Cantabria s\/n, 09006, Burgos , , durda@ubu.es","place":["Spain"]}]},{"given":"Bel\u00e9n","family":"Alonso","sequence":"additional","affiliation":[{"name":"Departamento de Qu\u00edmica , Escuela Polit\u00e9cnica Superior, , C\/Villadiego s\/n. 09001 Burgos , balonso@ubu.es"},{"name":"Universidad de Burgos , Escuela Polit\u00e9cnica Superior, , C\/Villadiego s\/n. 09001 Burgos , balonso@ubu.es"}]}],"member":"286","published-online":{"date-parts":[[2024,5,20]]},"reference":[{"key":"2025092510415181300_ref1","doi-asserted-by":"publisher","first-page":"101560","DOI":"10.1016\/j.resourpol.2019.101560","article-title":"Stationarity of prices of precious and industrial metals using recent unit root methods: Implications for markets\u2019 efficiency","volume":"65","author":"Adewuyi","year":"2020","journal-title":"Resources Policy"},{"key":"2025092510415181300_ref2","first-page":"62","article-title":"Forecasting steel prices using ARIMAX model: A case study of Turkey","volume":"4","author":"Adli","year":"2020","journal-title":"The International Journal of Business Management and Technology"},{"key":"2025092510415181300_ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-18050-7_18","article-title":"Non-linear neural models to predict HRC steel price in Spain","author":"Alcalde","year":"2023","journal-title":"Lecture Notes in Networks and Systems"},{"key":"2025092510415181300_ref4","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1007\/s10044-020-00872-x","article-title":"Analysing the intermeshed patterns of road transportation and macroeconomic indicators through neural and clustering techniques","volume":"23","author":"Alonso de Armi\u00f1o","year":"2020","journal-title":"Pattern Analysis and Applications"},{"key":"2025092510415181300_ref5","doi-asserted-by":"publisher","DOI":"10.1109\/UBMK.2019.8907015","article-title":"Steel price forcasting using long short-term memory network model","author":"Cetin","year":"2019","journal-title":"UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering"},{"key":"2025092510415181300_ref6","doi-asserted-by":"publisher","first-page":"109782","DOI":"10.1016\/j.jenvman.2019.109782","article-title":"A review of the current environmental challenges of the steel industry and its value chain","volume":"259","author":"Conejo","year":"2020","journal-title":"Journal of Environmental Management"},{"key":"2025092510415181300_ref7","volume-title":"Towards Competitive and Clean European Steel","author":"European Commission","year":"2021"},{"key":"2025092510415181300_ref8","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.resourpol.2018.11.014","article-title":"Steel product prices transmission activities in the midstream industrial chain and global markets","volume":"60","author":"Guo","year":"2019","journal-title":"Resources Policy"},{"key":"2025092510415181300_ref9","doi-asserted-by":"publisher","first-page":"100849","DOI":"10.1016\/j.najef.2018.09.007","article-title":"Price effects of steel commodities on worldwide stock market returns","volume":"51","author":"Gutierrez","year":"2020","journal-title":"North American Journal of Economics and Finance"},{"key":"2025092510415181300_ref10","doi-asserted-by":"publisher","first-page":"118750","DOI":"10.1016\/j.energy.2020.118750","article-title":"A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series","volume":"212","author":"Karasu","year":"2020","journal-title":"Energy"},{"key":"2025092510415181300_ref11","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1080\/0020718508961129","article-title":"Input-output parametric models for non-linear systems part I: Deterministic non-linear systems","volume":"41","author":"Leontaritis","year":"1985","journal-title":"International Journal of Control"},{"key":"2025092510415181300_ref12","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1134\/S1075700711030105","article-title":"Forecast of global steel prices","volume":"22","author":"Malanichev","year":"2011","journal-title":"Studies on Russian Economic Development"},{"key":"2025092510415181300_ref13","doi-asserted-by":"publisher","first-page":"207","DOI":"10.25019\/mdke\/7.2.05","article-title":"Challenging the status quo: Steel producer case study on the enterprise value for M&A","volume":"7","author":"Manu","year":"2019","journal-title":"Management Dynamics in the Knowledge Economy"},{"key":"2025092510415181300_ref14","doi-asserted-by":"publisher","first-page":"102769","DOI":"10.1016\/j.resourpol.2022.102769","article-title":"Dynamic strategic planning: A hybrid approach based on logarithmic regression, system dynamics, game theory and fuzzy inference system (case study steel industry)","volume":"77","author":"Mehmanpazir","year":"2022","journal-title":"Resources Policy"},{"key":"2025092510415181300_ref15","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.procir.2016.04.084","article-title":"Steel stock analysis in Europe from 1945 to 2013","volume":"48","author":"Panasiyk","year":"2016","journal-title":"Procedia CIRP"},{"key":"2025092510415181300_ref16","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.resconrec.2012.11.008","article-title":"Steel all over the world: Estimating in-use stocks of iron for 200 countries","volume":"71","author":"Pauliuk","year":"2013","journal-title":"Resources, Conservation and Recycling"},{"key":"2025092510415181300_ref17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/9928836","article-title":"An approach for demand forecasting in steel industries using ensemble learning","volume":"2022","author":"Raju","year":"2022","journal-title":"Complexity"},{"key":"2025092510415181300_ref18","doi-asserted-by":"publisher","first-page":"137","DOI":"10.15446\/cuad.econ.v38n76.61257","article-title":"Competencia, rendimientos crecientes y exceso de capacidad: la industria sider\u00fargica mundial (2000-2014)","volume":"38","author":"Rodr\u00edguez Liboreiro","year":"2019","journal-title":"Cuadernos de Econom\u00eda"},{"key":"2025092510415181300_ref19","article-title":"Review of Economics & Finance a Study of the dynamic relationship between crude oil Price","volume":"2","author":"Su","year":"2012","journal-title":"Better Advances Press, Canada in its journal Review of Economics & Finance"},{"key":"2025092510415181300_ref20","doi-asserted-by":"publisher","DOI":"10.1080\/23322039.2023.2169997","article-title":"Employing a generalized reduced gradient algorithm method to form combinations of steel price forecasts generated separately by ARIMA-TF and ANN models","volume":"11","author":"Terregrossa","year":"2023","journal-title":"Cogent Economics & Finance"},{"key":"2025092510415181300_ref21","doi-asserted-by":"publisher","first-page":"4951","DOI":"10.3390\/su15064951","article-title":"Research on a prediction model and influencing factors of cross-regional Price differences of rebar spot based on long short-term memory network","volume":"15","author":"Wu","year":"2023","journal-title":"Sustainability"},{"key":"2025092510415181300_ref22","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/s13563-022-00357-9","article-title":"Steel price index forecasting through neural networks: The composite index, long products, flat products, and rolled products","volume":"36","author":"Xu","year":"2023","journal-title":"Mineral Economics"},{"key":"2025092510415181300_ref23","doi-asserted-by":"publisher","first-page":"101547","DOI":"10.1016\/j.jocs.2021.101547","article-title":"Humidity forecasting in a potato plantation using time-series neural models","volume":"59","author":"Yartu","year":"2022","journal-title":"Journal of Computer Science"},{"key":"2025092510415181300_ref24","article-title":"Forecasting the steel product prices with the Arima model","volume":"14","author":"Zola","year":"2016","journal-title":"Statistica e Applicazioni"}],"container-title":["Logic Journal of the IGPL"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jigpal\/article-pdf\/33\/5\/jzae060\/57745615\/jzae060.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jigpal\/article-pdf\/33\/5\/jzae060\/57745615\/jzae060.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T14:41:58Z","timestamp":1758811318000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jigpal\/article\/doi\/10.1093\/jigpal\/jzae060\/7675184"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,20]]},"references-count":24,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,8,15]]}},"URL":"https:\/\/doi.org\/10.1093\/jigpal\/jzae060","relation":{},"ISSN":["1367-0751","1368-9894"],"issn-type":[{"value":"1367-0751","type":"print"},{"value":"1368-9894","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,10]]},"published":{"date-parts":[[2024,5,20]]},"article-number":"jzae060"}}