A. S. Minor, N. Polyakhov, I. Prikhodko, E. Vorobyova
{"title":"基于履带法- Ssa的临时行预报","authors":"A. S. Minor, N. Polyakhov, I. Prikhodko, E. Vorobyova","doi":"10.1109/SCM.2015.7190439","DOIUrl":null,"url":null,"abstract":"Based on the Visual representation of the results of the decomposition of the singular trajectory matrix time series that contains the data of power consumption for 28 days, the trend and the periodic component of the series. The accuracy of the prediction when you use the <;<;Caterpillar-SSA>> was 1.75 %.","PeriodicalId":106868,"journal":{"name":"2015 XVIII International Conference on Soft Computing and Measurements (SCM)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Forecasting of a temporary row on the basis of the caterpillar method — Ssa\",\"authors\":\"A. S. Minor, N. Polyakhov, I. Prikhodko, E. Vorobyova\",\"doi\":\"10.1109/SCM.2015.7190439\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the Visual representation of the results of the decomposition of the singular trajectory matrix time series that contains the data of power consumption for 28 days, the trend and the periodic component of the series. The accuracy of the prediction when you use the <;<;Caterpillar-SSA>> was 1.75 %.\",\"PeriodicalId\":106868,\"journal\":{\"name\":\"2015 XVIII International Conference on Soft Computing and Measurements (SCM)\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 XVIII International Conference on Soft Computing and Measurements (SCM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCM.2015.7190439\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 XVIII International Conference on Soft Computing and Measurements (SCM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCM.2015.7190439","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Forecasting of a temporary row on the basis of the caterpillar method — Ssa
Based on the Visual representation of the results of the decomposition of the singular trajectory matrix time series that contains the data of power consumption for 28 days, the trend and the periodic component of the series. The accuracy of the prediction when you use the <;<;Caterpillar-SSA>> was 1.75 %.
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