一种整合上下文知识的组合预测方法

Huang An-qiang, Wang. Shouyang
{"title":"一种整合上下文知识的组合预测方法","authors":"Huang An-qiang, Wang. Shouyang","doi":"10.4018/978-1-4666-3998-0.ch019","DOIUrl":null,"url":null,"abstract":"According to Qian's meta-synthesis theory and TEI@I methodology,this paper proposes a combined forecasting method based on integrated contextual knowledge(CFMIK).Utilizing contextual knowledge to guide the forecasting process,this method can cover the influence of those factors that cannot be explicitly included in the forecasting model,and thus it can decrease the forecast error from stochastic events to some extent.Through a container throughput forecast case,this paper compares the performance of CFMIK,AFTER(a combined forecasting method) and 3 single models(ARIMA,BP-ANN, Exponential Smoothing).The results show that the performance of CFMIK is better than that of the remaining ones.","PeriodicalId":101206,"journal":{"name":"Systems Engineering - Theory & Practice","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A combined forecasting method integrating contextual knowledge\",\"authors\":\"Huang An-qiang, Wang. Shouyang\",\"doi\":\"10.4018/978-1-4666-3998-0.ch019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"According to Qian's meta-synthesis theory and TEI@I methodology,this paper proposes a combined forecasting method based on integrated contextual knowledge(CFMIK).Utilizing contextual knowledge to guide the forecasting process,this method can cover the influence of those factors that cannot be explicitly included in the forecasting model,and thus it can decrease the forecast error from stochastic events to some extent.Through a container throughput forecast case,this paper compares the performance of CFMIK,AFTER(a combined forecasting method) and 3 single models(ARIMA,BP-ANN, Exponential Smoothing).The results show that the performance of CFMIK is better than that of the remaining ones.\",\"PeriodicalId\":101206,\"journal\":{\"name\":\"Systems Engineering - Theory & Practice\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems Engineering - Theory & Practice\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/978-1-4666-3998-0.ch019\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Engineering - Theory & Practice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-4666-3998-0.ch019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

根据Qian的元综合理论和TEI@I方法,本文提出了一种基于整合语境知识(CFMIK)的组合预测方法。该方法利用上下文知识指导预测过程,可以覆盖预测模型中不能明确包含的因素的影响,从而在一定程度上减小随机事件的预测误差。通过一个集装箱吞吐量预测案例,比较了CFMIK、AFTER(一种组合预测方法)和3种单一模型(ARIMA、BP-ANN、指数平滑)的性能。结果表明,CFMIK的性能优于其他几种。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
A combined forecasting method integrating contextual knowledge
According to Qian's meta-synthesis theory and TEI@I methodology,this paper proposes a combined forecasting method based on integrated contextual knowledge(CFMIK).Utilizing contextual knowledge to guide the forecasting process,this method can cover the influence of those factors that cannot be explicitly included in the forecasting model,and thus it can decrease the forecast error from stochastic events to some extent.Through a container throughput forecast case,this paper compares the performance of CFMIK,AFTER(a combined forecasting method) and 3 single models(ARIMA,BP-ANN, Exponential Smoothing).The results show that the performance of CFMIK is better than that of the remaining ones.
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
期刊最新文献
Information technology and systems Book review editorial Book review editorial A combined forecasting method integrating contextual knowledge Personal Credit Risk Measurement: Bilateral Antibody Artificial Immune Probability Model
×
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