000127938 001__ 127938
000127938 005__ 20240731103347.0
000127938 0247_ $$2doi$$a10.1007/s00521-023-08730-7
000127938 0248_ $$2sideral$$a135113
000127938 037__ $$aART-2023-135113
000127938 041__ $$aeng
000127938 100__ $$aSuaza-Medina, Mario E.$$uUniversidad de Zaragoza
000127938 245__ $$aEffects of data time lag in a decision-making system using machine learning for pork price prediction
000127938 260__ $$c2023
000127938 5060_ $$aAccess copy available to the general public$$fUnrestricted
000127938 5203_ $$aSpain is the third-largest producer of pork meat in the world, and many farms in several regions depend on the evolution of this market. However, the current pricing system is unfair, as some actors have better market information than others. In this context, historical pricing is an easy-to-find and affordable data source that can help all agents to be better informed. However, the time lag in data acquisition can affect their pricing decisions. In this paper, we study the effect that data acquisition delay has on a price prediction system using multiple prediction algorithms. We describe the integration of the best proposal into a decision support system prototype and test it in a real-case scenario. Specifically, we use public data from the most important regional pork meat markets in Spain published by the Ministry of Agriculture with a two-week delay and subscription-based data of the same markets obtained on the same day. The results show that the error difference between the best public and data subscription models is 0.6 Euro cents in favour of the data without delay. The market dimension makes these differences significant in the supply chain, giving pricing agents a better tool to negotiate market prices.
000127938 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-113353RB-I00$$9info:eu-repo/grantAgreement/ES/DGA/T59-23R
000127938 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000127938 590__ $$a4.5$$b2023
000127938 592__ $$a1.256$$b2023
000127938 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b52 / 197 = 0.264$$c2023$$dQ2$$eT1
000127938 593__ $$aSoftware$$c2023$$dQ1
000127938 593__ $$aArtificial Intelligence$$c2023$$dQ2
000127938 594__ $$a11.4$$b2023
000127938 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000127938 700__ $$0(orcid)0000-0002-6557-2494$$aZarazaga-Soria, F. Javier$$uUniversidad de Zaragoza
000127938 700__ $$aPinilla-Lopez, Jorge
000127938 700__ $$0(orcid)0000-0001-6491-7430$$aLopez-Pellicer, Francisco J.$$uUniversidad de Zaragoza
000127938 700__ $$0(orcid)0000-0003-3071-5819$$aLacasta, Javier$$uUniversidad de Zaragoza
000127938 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000127938 773__ $$g35, 26 (2023), 19221-19233$$pNeural comput. appl.$$tNeural Computing and Applications$$x0941-0643
000127938 8564_ $$s869197$$uhttps://zaguan.unizar.es/record/127938/files/texto_completo.pdf$$yVersión publicada
000127938 8564_ $$s2294469$$uhttps://zaguan.unizar.es/record/127938/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000127938 909CO $$ooai:zaguan.unizar.es:127938$$particulos$$pdriver
000127938 951__ $$a2024-07-31-09:52:59
000127938 980__ $$aARTICLE