周期性ARMA模型应用于每周流量预报

M. Maceira, J. M. Damázio, A. Ghirardi, H. Dantas
{"title":"周期性ARMA模型应用于每周流量预报","authors":"M. Maceira, J. M. Damázio, A. Ghirardi, H. Dantas","doi":"10.1109/PTC.1999.826517","DOIUrl":null,"url":null,"abstract":"This paper presents a weekly streamflow forecasting model based on linear ARMA (p, q) models, considering both periodic and nonperiodic models. For each week, fifty possible models are automatically analyzed. The best modeling and parameter estimation are chosen based on the minimum square mean forecast error of the whole time series. The proposed model, which has been validated by the Brazilian Multi-Utility Hydrological Studies Working Group, is illustrated in case studies with several hydraulic plants of the Brazilian Southern, Southeastern, North and Northeastern systems.","PeriodicalId":101688,"journal":{"name":"PowerTech Budapest 99. Abstract Records. (Cat. No.99EX376)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Periodic ARMA models applied to weekly streamflow forecasts\",\"authors\":\"M. Maceira, J. M. Damázio, A. Ghirardi, H. Dantas\",\"doi\":\"10.1109/PTC.1999.826517\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a weekly streamflow forecasting model based on linear ARMA (p, q) models, considering both periodic and nonperiodic models. For each week, fifty possible models are automatically analyzed. The best modeling and parameter estimation are chosen based on the minimum square mean forecast error of the whole time series. The proposed model, which has been validated by the Brazilian Multi-Utility Hydrological Studies Working Group, is illustrated in case studies with several hydraulic plants of the Brazilian Southern, Southeastern, North and Northeastern systems.\",\"PeriodicalId\":101688,\"journal\":{\"name\":\"PowerTech Budapest 99. Abstract Records. (Cat. No.99EX376)\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PowerTech Budapest 99. Abstract Records. (Cat. No.99EX376)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PTC.1999.826517\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PowerTech Budapest 99. Abstract Records. (Cat. No.99EX376)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PTC.1999.826517","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

摘要

本文提出了一种基于线性ARMA (p, q)模型的周流量预测模型,同时考虑了周期模型和非周期模型。每周,系统自动分析50种可能的模型。基于整个时间序列预测误差的最小二乘平均值选择最佳建模和参数估计。所提出的模型已得到巴西多用途水文研究工作组的验证,并在巴西南部、东南部、北部和东北部系统的几个水力发电厂的案例研究中得到说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Periodic ARMA models applied to weekly streamflow forecasts
This paper presents a weekly streamflow forecasting model based on linear ARMA (p, q) models, considering both periodic and nonperiodic models. For each week, fifty possible models are automatically analyzed. The best modeling and parameter estimation are chosen based on the minimum square mean forecast error of the whole time series. The proposed model, which has been validated by the Brazilian Multi-Utility Hydrological Studies Working Group, is illustrated in case studies with several hydraulic plants of the Brazilian Southern, Southeastern, North and Northeastern systems.
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
自引率
0.00%
发文量
0
×
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