{"title":"Evaluation of multivariate transductive neuro-fuzzy inference system for multivariate time-series analysis and modelling","authors":"H. Widiputra","doi":"10.1145/3427423.3427428","DOIUrl":null,"url":null,"abstract":"Multivariate Transductive Neuro-Fuzzy Inference System model, named the mTNFI is a previously proposed conceptual transductive approach designed for analysis and modelling of multivariate time-series data. In this study, we revisit, implement and evaluate the mTNFI model potential for patterns of relationship extraction from a collection of interrelated time-series data. Results of conducted assessment confirm the mTNFI capability in recognizing patterns of relationship and then to model them in human-readable form, i.e. fuzzy rules. Additionally, a comparative analysis also show the superiority of the mTNFI model in comparison to other widely known time-series forecasting techniques when being used to predict future values.","PeriodicalId":120194,"journal":{"name":"Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3427423.3427428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Multivariate Transductive Neuro-Fuzzy Inference System model, named the mTNFI is a previously proposed conceptual transductive approach designed for analysis and modelling of multivariate time-series data. In this study, we revisit, implement and evaluate the mTNFI model potential for patterns of relationship extraction from a collection of interrelated time-series data. Results of conducted assessment confirm the mTNFI capability in recognizing patterns of relationship and then to model them in human-readable form, i.e. fuzzy rules. Additionally, a comparative analysis also show the superiority of the mTNFI model in comparison to other widely known time-series forecasting techniques when being used to predict future values.
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