Youdthachai Lertworaprachaya, Yingjie Yang, R. John
{"title":"High-order Type-2 fuzzy time series","authors":"Youdthachai Lertworaprachaya, Yingjie Yang, R. John","doi":"10.1109/SOCPAR.2010.5686732","DOIUrl":null,"url":null,"abstract":"This paper proposes a novel improved model for fuzzy time series forecasting using high-order and Type-2 fuzzy time series. Some Type-1 fuzzy time series model based on first order and high-order fuzzy time series have already been developed, however the accuracy of the forecasting values sill needs to be improved. In this paper, we present a high-order fuzzy time series model using Type-2 fuzzy sets. The employment of Type-2 fuzzy sets enables the proposed method to achieve a better result compared with existing fuzzy time series models. Our experimental results demonstrate the improved forecasting accuracy and reliability in comparison with other existing fuzzy time series models.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference of Soft Computing and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SOCPAR.2010.5686732","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
This paper proposes a novel improved model for fuzzy time series forecasting using high-order and Type-2 fuzzy time series. Some Type-1 fuzzy time series model based on first order and high-order fuzzy time series have already been developed, however the accuracy of the forecasting values sill needs to be improved. In this paper, we present a high-order fuzzy time series model using Type-2 fuzzy sets. The employment of Type-2 fuzzy sets enables the proposed method to achieve a better result compared with existing fuzzy time series models. Our experimental results demonstrate the improved forecasting accuracy and reliability in comparison with other existing fuzzy time series models.