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 favor 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.

翻译：西班牙是全球第三大猪肉生产国，其多个地区的大量农场都依赖这一市场的动态变化。然而，当前的定价体系存在不公平现象，因为部分市场参与者掌握着更优的市场信息。在此背景下，历史价格作为一种易于获取且成本低廉的数据来源，有助于所有市场参与者获得更充分的信息。然而，数据获取的时滞性可能影响其定价决策。本文研究了数据采集延迟对采用多种预测算法的价格预测系统的影响。我们将最优方案集成到决策支持系统原型中，并在实际场景下进行了测试。具体而言，我们使用了西班牙农业部发布的、具有两周延迟的全国主要猪肉区域市场公开数据，以及同日获取的同一市场的订阅数据。结果表明，最佳公开数据模型与订阅数据模型的误差差异为0.6欧分，且无延迟数据模型表现更优。市场规模的扩大使得这些差异在整个供应链中具有显著影响，为定价参与者提供了更优的市场议价工具。