{"title":"Introducing Flow Field Forecasting","authors":"Michael Frey, Kyle A. Caudle","doi":"10.1109/ICMLA.2011.82","DOIUrl":null,"url":null,"abstract":"A machine learning methodology, called flow field forecasting, is proposed for statistically predicting the future of a univariate time series. Flow field forecasting draws information from the interpolated flow field of an observed time series to build a forecast step-by-step. Flow field forecasting is presented with examples, a discussion of its properties relative to other common forecasting techniques, and a statistical error analysis.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"332 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 10th International Conference on Machine Learning and Applications and Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2011.82","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
A machine learning methodology, called flow field forecasting, is proposed for statistically predicting the future of a univariate time series. Flow field forecasting draws information from the interpolated flow field of an observed time series to build a forecast step-by-step. Flow field forecasting is presented with examples, a discussion of its properties relative to other common forecasting techniques, and a statistical error analysis.