Onlineforecast: An R Package for Adaptive and Recursive Forecasting

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS R Journal Pub Date : 2023-09-07 DOI:10.32614/rj-2023-031
Bacher, Peder, Bergsteinsson, Hjörleifur G., Frölke, Linde, Sørensen, Mikkel L., Lemos-Vinasco, Julian, Liisberg, Jon, Møller, Jan Kloppenborg, Nielsen, Henrik Aalborg, Madsen, Henrik
{"title":"Onlineforecast: An R Package for Adaptive and Recursive Forecasting","authors":"Bacher, Peder, Bergsteinsson, Hjörleifur G., Frölke, Linde, Sørensen, Mikkel L., Lemos-Vinasco, Julian, Liisberg, Jon, Møller, Jan Kloppenborg, Nielsen, Henrik Aalborg, Madsen, Henrik","doi":"10.32614/rj-2023-031","DOIUrl":null,"url":null,"abstract":"Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the [R]{.sans-serif} package [[onlineforecast](https://onlineforecasting.org)]{.sans-serif} that provides a generalized setup of data and models for online forecasting. It has functionality for time-adaptive fitting of dynamical and non-linear models. The setup is tailored to enable the effective use of forecasts as model inputs, e.g. numerical weather forecast. Users can create new models for their particular applications and run models in an operational setting. The package also allows users to easily replace parts of the setup, e.g. using new methods for estimation. The package comes with comprehensive vignettes and examples of online forecasting applications in energy systems, but can easily be applied for online forecasting in all fields.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"21 1","pages":"0"},"PeriodicalIF":2.3000,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"R Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32614/rj-2023-031","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the [R]{.sans-serif} package [[onlineforecast](https://onlineforecasting.org)]{.sans-serif} that provides a generalized setup of data and models for online forecasting. It has functionality for time-adaptive fitting of dynamical and non-linear models. The setup is tailored to enable the effective use of forecasts as model inputs, e.g. numerical weather forecast. Users can create new models for their particular applications and run models in an operational setting. The package also allows users to easily replace parts of the setup, e.g. using new methods for estimation. The package comes with comprehensive vignettes and examples of online forecasting applications in energy systems, but can easily be applied for online forecasting in all fields.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
在线预测:一个R包自适应和递归预测
依赖预测做出决策的系统,如e.g.Â控制系统或能源交易系统,需要经常更新预测。通常,只要有新的观测资料,预报就会更新,因此是在线的。我们提出[R]{。Sans-serif}包[[onlinefforecast](https://onlineforecasting.org)]{. Sans-serif},它为在线预测提供了一个通用的数据和模型设置。它具有动态和非线性模型的时间自适应拟合功能。设置是量身定制的,以便有效地使用预测作为模式输入,e.g.Â数值天气预报。用户可以为他们的特定应用程序创建新模型,并在操作设置中运行模型。该软件包还允许用户轻松替换部分设置,e.g.Â使用新的估算方法。该软件包附带了能源系统在线预测应用的综合插图和示例,但可以很容易地应用于所有领域的在线预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
期刊最新文献
binGroup2: Statistical Tools for Infection Identification via Group Testing. glmmPen: High Dimensional Penalized Generalized Linear Mixed Models. Three-Way Correspondence Analysis in R nlstac: Non-Gradient Separable Nonlinear Least Squares Fitting A Workflow for Estimating and Visualising Excess Mortality During the COVID-19 Pandemic
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1