{"title":"基于卷积神经网络的时间序列数据未知模式检测方法","authors":"J. Bao, Xinyi Li","doi":"10.1109/IICSPI48186.2019.9095913","DOIUrl":null,"url":null,"abstract":"Exploration on the time series data in unknown model pattern recognition has important research significance. This paper proposes an unknown pattern detection method for time-series data based on convolution neural network, which planifies the output results by transforming fully connection layer and softmax layer of the traditional convolutional neural network, and uses the coordinate point and Euclidean distance to determine whether the timing series data belongs to the known pattern or the unknown pattern. Experiments show that the method in this paper can effectively detect the time-series data of unknown patterns and has certain accuracy.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Unknown Pattern Detection Method for Time Series Data Based on Convolutional Neural Network\",\"authors\":\"J. Bao, Xinyi Li\",\"doi\":\"10.1109/IICSPI48186.2019.9095913\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Exploration on the time series data in unknown model pattern recognition has important research significance. This paper proposes an unknown pattern detection method for time-series data based on convolution neural network, which planifies the output results by transforming fully connection layer and softmax layer of the traditional convolutional neural network, and uses the coordinate point and Euclidean distance to determine whether the timing series data belongs to the known pattern or the unknown pattern. Experiments show that the method in this paper can effectively detect the time-series data of unknown patterns and has certain accuracy.\",\"PeriodicalId\":318693,\"journal\":{\"name\":\"2019 2nd International Conference on Safety Produce Informatization (IICSPI)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 2nd International Conference on Safety Produce Informatization (IICSPI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IICSPI48186.2019.9095913\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IICSPI48186.2019.9095913","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Unknown Pattern Detection Method for Time Series Data Based on Convolutional Neural Network
Exploration on the time series data in unknown model pattern recognition has important research significance. This paper proposes an unknown pattern detection method for time-series data based on convolution neural network, which planifies the output results by transforming fully connection layer and softmax layer of the traditional convolutional neural network, and uses the coordinate point and Euclidean distance to determine whether the timing series data belongs to the known pattern or the unknown pattern. Experiments show that the method in this paper can effectively detect the time-series data of unknown patterns and has certain accuracy.