{"title":"作为分段平稳过程的时间序列模型","authors":"N. Ivanov, A. Prasolov","doi":"10.1145/3274856.3274888","DOIUrl":null,"url":null,"abstract":"This work is devoted to determining the space-time area that contains a time-series trend to address mathematical expectations. A time series trajectory represents the realization of a stochastic process in discrete time; thus, its approximation is random and cannot be considered a trend valuation. We offer to interpret an arbitrary time series as a trajectory of a piecewise-stationary process. This allows for us to describe an algorithm that constructs the area where the trend is located.","PeriodicalId":373840,"journal":{"name":"Proceedings of the 3rd International Conference on Applications in Information Technology","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Model of Time Series as a Piecewise-Stationary Process\",\"authors\":\"N. Ivanov, A. Prasolov\",\"doi\":\"10.1145/3274856.3274888\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work is devoted to determining the space-time area that contains a time-series trend to address mathematical expectations. A time series trajectory represents the realization of a stochastic process in discrete time; thus, its approximation is random and cannot be considered a trend valuation. We offer to interpret an arbitrary time series as a trajectory of a piecewise-stationary process. This allows for us to describe an algorithm that constructs the area where the trend is located.\",\"PeriodicalId\":373840,\"journal\":{\"name\":\"Proceedings of the 3rd International Conference on Applications in Information Technology\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd International Conference on Applications in Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3274856.3274888\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd International Conference on Applications in Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3274856.3274888","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Model of Time Series as a Piecewise-Stationary Process
This work is devoted to determining the space-time area that contains a time-series trend to address mathematical expectations. A time series trajectory represents the realization of a stochastic process in discrete time; thus, its approximation is random and cannot be considered a trend valuation. We offer to interpret an arbitrary time series as a trajectory of a piecewise-stationary process. This allows for us to describe an algorithm that constructs the area where the trend is located.