{"title":"通过模糊推理系统进行时间序列预测的工具","authors":"F. Montesino, A. Lendasse, A. Barriga","doi":"10.1109/IS.2008.4670398","DOIUrl":null,"url":null,"abstract":"A new software tool for time series prediction by means of fuzzy inference systems is reported. This tool, named xftsp, implements a novel methodology for time series prediction based on methods for automatic fuzzy systems identification and supervised learning combined with statistical methods for nonparametric residual variance estimation. xftsp is designed as a tool integrated in the Xfuzzy development environment for fuzzy systems. Experiments carried out on a number of time series benchmarks show the advantages of xftsp in terms of both accuracy and computational requirements as compared against Least-Squared Support Vector Machines, an established technique in the field of time series prediction.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"xftsp: A tool for time series prediction by means of fuzzy inference systems\",\"authors\":\"F. Montesino, A. Lendasse, A. Barriga\",\"doi\":\"10.1109/IS.2008.4670398\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new software tool for time series prediction by means of fuzzy inference systems is reported. This tool, named xftsp, implements a novel methodology for time series prediction based on methods for automatic fuzzy systems identification and supervised learning combined with statistical methods for nonparametric residual variance estimation. xftsp is designed as a tool integrated in the Xfuzzy development environment for fuzzy systems. Experiments carried out on a number of time series benchmarks show the advantages of xftsp in terms of both accuracy and computational requirements as compared against Least-Squared Support Vector Machines, an established technique in the field of time series prediction.\",\"PeriodicalId\":305750,\"journal\":{\"name\":\"2008 4th International IEEE Conference Intelligent Systems\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 4th International IEEE Conference Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IS.2008.4670398\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 4th International IEEE Conference Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IS.2008.4670398","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
摘要
本文报道了一种利用模糊推理系统进行时间序列预测的新软件工具。这个名为xftsp的工具实现了一种新的时间序列预测方法,该方法基于自动模糊系统识别方法和监督学习方法,结合非参数残差估计的统计方法。xftsp是一个集成在Xfuzzy开发环境中的模糊系统开发工具。在许多时间序列基准上进行的实验表明,与时间序列预测领域的一种成熟技术——最小二乘支持向量机(Least-Squared Support Vector Machines)相比,xftsp在准确性和计算需求方面都具有优势。
xftsp: A tool for time series prediction by means of fuzzy inference systems
A new software tool for time series prediction by means of fuzzy inference systems is reported. This tool, named xftsp, implements a novel methodology for time series prediction based on methods for automatic fuzzy systems identification and supervised learning combined with statistical methods for nonparametric residual variance estimation. xftsp is designed as a tool integrated in the Xfuzzy development environment for fuzzy systems. Experiments carried out on a number of time series benchmarks show the advantages of xftsp in terms of both accuracy and computational requirements as compared against Least-Squared Support Vector Machines, an established technique in the field of time series prediction.