{"title":"作为缺失值问题的时间序列预测:在ESTSP2007和NN3竞争基准中的应用","authors":"A. Sorjamaa, A. Lendasse","doi":"10.1109/IJCNN.2007.4371429","DOIUrl":null,"url":null,"abstract":"In this paper, time series prediction is considered as a problem of missing values. A method for the determination of the missing time series values is presented. The method is based on two projection methods: a nonlinear one (Self-Organized Maps) and a linear one (Empirical Orthogonal Functions). The presented global methodology combines the advantages of both methods to get accurate candidates for the prediction values. The methods are applied to two time series competition datasets.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"642 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Time Series Prediction as a Problem of Missing Values: Application to ESTSP2007 and NN3 Competition Benchmarks\",\"authors\":\"A. Sorjamaa, A. Lendasse\",\"doi\":\"10.1109/IJCNN.2007.4371429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, time series prediction is considered as a problem of missing values. A method for the determination of the missing time series values is presented. The method is based on two projection methods: a nonlinear one (Self-Organized Maps) and a linear one (Empirical Orthogonal Functions). The presented global methodology combines the advantages of both methods to get accurate candidates for the prediction values. The methods are applied to two time series competition datasets.\",\"PeriodicalId\":350091,\"journal\":{\"name\":\"2007 International Joint Conference on Neural Networks\",\"volume\":\"642 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.2007.4371429\",\"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 International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2007.4371429","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Time Series Prediction as a Problem of Missing Values: Application to ESTSP2007 and NN3 Competition Benchmarks
In this paper, time series prediction is considered as a problem of missing values. A method for the determination of the missing time series values is presented. The method is based on two projection methods: a nonlinear one (Self-Organized Maps) and a linear one (Empirical Orthogonal Functions). The presented global methodology combines the advantages of both methods to get accurate candidates for the prediction values. The methods are applied to two time series competition datasets.