{"title":"基于OWA算子的部分趋势聚合的时间序列语言摘要","authors":"J. Kacprzyk, A. Wilbik, S. Zadrożny","doi":"10.1109/FUZZY.2007.4295411","DOIUrl":null,"url":null,"abstract":"We extend our approach to the linguistic summarization of (numerical) time series. The main issue boils down to the identification of trends in time series that are characterized by a set of attributes followed by their appropriate aggregation. We propose to use the OWA (ordered weighted averaging) operators for the aggregation of partial trends as an alternative to the use of the classic Zadeh's calculus of linguistically quantified propositions, the Sugeno integral and the Choquet integral. The use of the OWA operators provides a convenient unified aggregation means that can be used to derive diverse types of summaries. The results obtained confirm a high human consistency of linguistic summaries derived.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"Linguistic Summaries of Time Series via an OWA Operator Based Aggregation of Partial Trends\",\"authors\":\"J. Kacprzyk, A. Wilbik, S. Zadrożny\",\"doi\":\"10.1109/FUZZY.2007.4295411\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We extend our approach to the linguistic summarization of (numerical) time series. The main issue boils down to the identification of trends in time series that are characterized by a set of attributes followed by their appropriate aggregation. We propose to use the OWA (ordered weighted averaging) operators for the aggregation of partial trends as an alternative to the use of the classic Zadeh's calculus of linguistically quantified propositions, the Sugeno integral and the Choquet integral. The use of the OWA operators provides a convenient unified aggregation means that can be used to derive diverse types of summaries. The results obtained confirm a high human consistency of linguistic summaries derived.\",\"PeriodicalId\":236515,\"journal\":{\"name\":\"2007 IEEE International Fuzzy Systems Conference\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Fuzzy Systems Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.2007.4295411\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Fuzzy Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.2007.4295411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Linguistic Summaries of Time Series via an OWA Operator Based Aggregation of Partial Trends
We extend our approach to the linguistic summarization of (numerical) time series. The main issue boils down to the identification of trends in time series that are characterized by a set of attributes followed by their appropriate aggregation. We propose to use the OWA (ordered weighted averaging) operators for the aggregation of partial trends as an alternative to the use of the classic Zadeh's calculus of linguistically quantified propositions, the Sugeno integral and the Choquet integral. The use of the OWA operators provides a convenient unified aggregation means that can be used to derive diverse types of summaries. The results obtained confirm a high human consistency of linguistic summaries derived.