{"title":"用模糊本体解释时间序列异常的定义","authors":"D. Kurilo, V. Moshkin, I. Andreev, N. Yarushkina","doi":"10.1109/ITNT57377.2023.10139035","DOIUrl":null,"url":null,"abstract":"The paper describes an approach to detecting time series anomalies, taking into account the specifics of the subject area, represented as a fuzzy ontology. The approach involves the use of LSTM (long short-term memory) networks for the mathematical search for anomalies, fuzzy ontology allows you to filter the detection results and draw an inference for decision making.","PeriodicalId":296438,"journal":{"name":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Interpreting the definition of time series anomalies using fuzzy ontologies\",\"authors\":\"D. Kurilo, V. Moshkin, I. Andreev, N. Yarushkina\",\"doi\":\"10.1109/ITNT57377.2023.10139035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper describes an approach to detecting time series anomalies, taking into account the specifics of the subject area, represented as a fuzzy ontology. The approach involves the use of LSTM (long short-term memory) networks for the mathematical search for anomalies, fuzzy ontology allows you to filter the detection results and draw an inference for decision making.\",\"PeriodicalId\":296438,\"journal\":{\"name\":\"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITNT57377.2023.10139035\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IX International Conference on Information Technology and Nanotechnology (ITNT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITNT57377.2023.10139035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interpreting the definition of time series anomalies using fuzzy ontologies
The paper describes an approach to detecting time series anomalies, taking into account the specifics of the subject area, represented as a fuzzy ontology. The approach involves the use of LSTM (long short-term memory) networks for the mathematical search for anomalies, fuzzy ontology allows you to filter the detection results and draw an inference for decision making.