融合直觉模糊认知图谱和模糊神经网络的混合区间值时间序列预测模型

IF 3.4 3区 经济学 Q1 ECONOMICS
Jiajia Zhang, Zhifu Tao, Jinpei Liu, Xi Liu, Huayou Chen
{"title":"融合直觉模糊认知图谱和模糊神经网络的混合区间值时间序列预测模型","authors":"Jiajia Zhang, Zhifu Tao, Jinpei Liu, Xi Liu, Huayou Chen","doi":"10.1002/for.3181","DOIUrl":null,"url":null,"abstract":"The definition of interval‐valued time series is now a valid tool that can be used to model uncertainty with known numerical bounds. However, how to provide accurate predictions of interval‐valued time series remains an open problem. The goal of this paper is to develop a hybrid interval‐valued time series prediction model that incorporates an intuitionistic fuzzy cognitive map and a fuzzy neural network. The causal relationship and adjacency matrix among nodes of the intuitionistic fuzzy cognitive map are defined and quantified using mutual subsethhood, in which the hesitation weight is added to the connection weight among concept nodes. The approach directly constructs concept nodes and a weight matrix for automatic recognition of intuitionistic fuzzy cognitive maps from original sequence data and combines the particle swarm optimization algorithm and back propagation algorithm to run with less manual intervention. The confidence intervals of forecasted interval values are also discussed. The developed prediction model is applied to forecast interval‐valued financial time series (i.e., the Nasdaq‐100 stock index), which is composed of daily minimum price and maximum price. The feasibility and validity of the proposed developed prediction model are shown through comparisons with some existing prediction models on interval‐valued time series.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"51 1","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A hybrid interval‐valued time series prediction model incorporating intuitionistic fuzzy cognitive map and fuzzy neural network\",\"authors\":\"Jiajia Zhang, Zhifu Tao, Jinpei Liu, Xi Liu, Huayou Chen\",\"doi\":\"10.1002/for.3181\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The definition of interval‐valued time series is now a valid tool that can be used to model uncertainty with known numerical bounds. However, how to provide accurate predictions of interval‐valued time series remains an open problem. The goal of this paper is to develop a hybrid interval‐valued time series prediction model that incorporates an intuitionistic fuzzy cognitive map and a fuzzy neural network. The causal relationship and adjacency matrix among nodes of the intuitionistic fuzzy cognitive map are defined and quantified using mutual subsethhood, in which the hesitation weight is added to the connection weight among concept nodes. The approach directly constructs concept nodes and a weight matrix for automatic recognition of intuitionistic fuzzy cognitive maps from original sequence data and combines the particle swarm optimization algorithm and back propagation algorithm to run with less manual intervention. The confidence intervals of forecasted interval values are also discussed. The developed prediction model is applied to forecast interval‐valued financial time series (i.e., the Nasdaq‐100 stock index), which is composed of daily minimum price and maximum price. The feasibility and validity of the proposed developed prediction model are shown through comparisons with some existing prediction models on interval‐valued time series.\",\"PeriodicalId\":47835,\"journal\":{\"name\":\"Journal of Forecasting\",\"volume\":\"51 1\",\"pages\":\"\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1002/for.3181\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1002/for.3181","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0

摘要

区间值时间序列的定义现已成为一种有效的工具,可用来建立具有已知数值界限的不确定性模型。然而,如何准确预测区间值时间序列仍是一个未决问题。本文的目标是开发一种混合区间值时间序列预测模型,该模型结合了直觉模糊认知图和模糊神经网络。直觉模糊认知图的因果关系和节点间的邻接矩阵是通过互子性定义和量化的,其中犹豫权重被添加到概念节点间的连接权重中。该方法直接构建概念节点和权重矩阵,用于从原始序列数据中自动识别直觉模糊认知图,并结合了粒子群优化算法和反向传播算法,运行时只需较少的人工干预。此外,还讨论了预测区间值的置信区间。所开发的预测模型被应用于预测由每日最低价和最高价组成的区间值金融时间序列(即纳斯达克 100 股票指数)。通过与一些现有的区间值时间序列预测模型进行比较,证明了所开发预测模型的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid interval‐valued time series prediction model incorporating intuitionistic fuzzy cognitive map and fuzzy neural network
The definition of interval‐valued time series is now a valid tool that can be used to model uncertainty with known numerical bounds. However, how to provide accurate predictions of interval‐valued time series remains an open problem. The goal of this paper is to develop a hybrid interval‐valued time series prediction model that incorporates an intuitionistic fuzzy cognitive map and a fuzzy neural network. The causal relationship and adjacency matrix among nodes of the intuitionistic fuzzy cognitive map are defined and quantified using mutual subsethhood, in which the hesitation weight is added to the connection weight among concept nodes. The approach directly constructs concept nodes and a weight matrix for automatic recognition of intuitionistic fuzzy cognitive maps from original sequence data and combines the particle swarm optimization algorithm and back propagation algorithm to run with less manual intervention. The confidence intervals of forecasted interval values are also discussed. The developed prediction model is applied to forecast interval‐valued financial time series (i.e., the Nasdaq‐100 stock index), which is composed of daily minimum price and maximum price. The feasibility and validity of the proposed developed prediction model are shown through comparisons with some existing prediction models on interval‐valued time series.
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
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
×
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