预测潮汐:了解巴西东北部大黄鱼价格的深度学习方法

IF 2.1 4区 环境科学与生态学 Q3 ECOLOGY
Vinícius Fellype Cavalcanti de França , Lucas Vinícius Santos Silva , Luan Diego de Oliveira , Marcela Gabriely Gomes da Silva , Humber Agrelli de Andrade
{"title":"预测潮汐:了解巴西东北部大黄鱼价格的深度学习方法","authors":"Vinícius Fellype Cavalcanti de França ,&nbsp;Lucas Vinícius Santos Silva ,&nbsp;Luan Diego de Oliveira ,&nbsp;Marcela Gabriely Gomes da Silva ,&nbsp;Humber Agrelli de Andrade","doi":"10.1016/j.rsma.2024.103932","DOIUrl":null,"url":null,"abstract":"<div><div>Seafood represents the most traded animal protein globally, with a significant contribution to food security in emerging economies. Therefore, it is crucial to conduct studies that aim to predict fluctuations in price to ensure the affordability of these products. Such studies could inform the establishment of political measures designed to minimize large variations in prices. In this context, the present research aimed to evaluate the historical price series, trends and seasonality of the whitemouth croaker traded in a supply center in northeastern Brazil. In addition, we tested the predictability capacity of a long short-term memory (LSTM) neural network in the context of seafood economic analysis. The prices exhibited a general upward trend, with occasional declines, and a more pronounced seasonal impact in recent years. The LSTM demonstrated low error scores of root mean square error, mean absolute error and mean absolute percentage error, indicating its suitability as a tool for monitoring the fluctuations in commodity prices. Nevertheless, adhering to certain standards is essential to prevent erroneous predictions that could result in misguided policy decisions.</div></div>","PeriodicalId":21070,"journal":{"name":"Regional Studies in Marine Science","volume":"81 ","pages":"Article 103932"},"PeriodicalIF":2.1000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting the tide: A deep-learning approach for understanding the whitemouth croaker prices in Northeast Brazil\",\"authors\":\"Vinícius Fellype Cavalcanti de França ,&nbsp;Lucas Vinícius Santos Silva ,&nbsp;Luan Diego de Oliveira ,&nbsp;Marcela Gabriely Gomes da Silva ,&nbsp;Humber Agrelli de Andrade\",\"doi\":\"10.1016/j.rsma.2024.103932\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Seafood represents the most traded animal protein globally, with a significant contribution to food security in emerging economies. Therefore, it is crucial to conduct studies that aim to predict fluctuations in price to ensure the affordability of these products. Such studies could inform the establishment of political measures designed to minimize large variations in prices. In this context, the present research aimed to evaluate the historical price series, trends and seasonality of the whitemouth croaker traded in a supply center in northeastern Brazil. In addition, we tested the predictability capacity of a long short-term memory (LSTM) neural network in the context of seafood economic analysis. The prices exhibited a general upward trend, with occasional declines, and a more pronounced seasonal impact in recent years. The LSTM demonstrated low error scores of root mean square error, mean absolute error and mean absolute percentage error, indicating its suitability as a tool for monitoring the fluctuations in commodity prices. Nevertheless, adhering to certain standards is essential to prevent erroneous predictions that could result in misguided policy decisions.</div></div>\",\"PeriodicalId\":21070,\"journal\":{\"name\":\"Regional Studies in Marine Science\",\"volume\":\"81 \",\"pages\":\"Article 103932\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Regional Studies in Marine Science\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352485524005656\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Regional Studies in Marine Science","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352485524005656","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

海产品是全球交易量最大的动物蛋白,对新兴经济体的粮食安全有重大贡献。因此,开展旨在预测价格波动的研究以确保这些产品的可负担性至关重要。此类研究可为制定旨在尽量减少价格大幅波动的政治措施提供信息。在此背景下,本研究旨在评估巴西东北部一个供应中心白嘴黄花鱼交易的历史价格序列、趋势和季节性。此外,我们还测试了长短期记忆(LSTM)神经网络在海产品经济分析中的预测能力。价格总体呈上升趋势,偶有下降,近年来季节性影响更为明显。LSTM 在均方根误差、平均绝对误差和平均绝对百分比误差方面都表现出较低的误差分值,表明其适合作为监测商品价格波动的工具。不过,要防止错误预测导致错误的政策决策,必须遵守一定的标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the tide: A deep-learning approach for understanding the whitemouth croaker prices in Northeast Brazil
Seafood represents the most traded animal protein globally, with a significant contribution to food security in emerging economies. Therefore, it is crucial to conduct studies that aim to predict fluctuations in price to ensure the affordability of these products. Such studies could inform the establishment of political measures designed to minimize large variations in prices. In this context, the present research aimed to evaluate the historical price series, trends and seasonality of the whitemouth croaker traded in a supply center in northeastern Brazil. In addition, we tested the predictability capacity of a long short-term memory (LSTM) neural network in the context of seafood economic analysis. The prices exhibited a general upward trend, with occasional declines, and a more pronounced seasonal impact in recent years. The LSTM demonstrated low error scores of root mean square error, mean absolute error and mean absolute percentage error, indicating its suitability as a tool for monitoring the fluctuations in commodity prices. Nevertheless, adhering to certain standards is essential to prevent erroneous predictions that could result in misguided policy decisions.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Regional Studies in Marine Science
Regional Studies in Marine Science Agricultural and Biological Sciences-Ecology, Evolution, Behavior and Systematics
CiteScore
3.90
自引率
4.80%
发文量
336
审稿时长
69 days
期刊介绍: REGIONAL STUDIES IN MARINE SCIENCE will publish scientifically sound papers on regional aspects of maritime and marine resources in estuaries, coastal zones, continental shelf, the seas and oceans.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信