Effects of data time lag in a decision-making system using machine learning for pork price prediction

Suaza-Medina, Mario E. (Universidad de Zaragoza) ; Zarazaga-Soria, F. Javier (Universidad de Zaragoza) ; Pinilla-Lopez, Jorge ; Lopez-Pellicer, Francisco J. (Universidad de Zaragoza) ; Lacasta, Javier (Universidad de Zaragoza)

Resumen: Spain 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.

Idioma: Inglés

DOI: 10.1007/s00521-023-08730-7

Año: 2023

Publicado en: Neural Computing and Applications 35, 26 (2023), 19221-19233
ISSN: 0941-0643

Factor impacto JCR: 4.5 (2023)

Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 52 / 197 = 0.264 (2023) - Q2 - T1

Factor impacto CITESCORE: 11.4 - Artificial Intelligence (Q1) - Software (Q1)

Factor impacto SCIMAGO: 1.256 - Software (Q1) - Artificial Intelligence (Q2)

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-113353RB-I00
Financiación: info:eu-repo/grantAgreement/ES/DGA/T59-23R

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)