{"title":"Prediction of time varying composite sources by temporal fuzzy clustering","authors":"S. Policker, A. Geva","doi":"10.1109/SSP.2001.955289","DOIUrl":null,"url":null,"abstract":"We present a method for predicting non-stationary signals generated by a time varying composite source. The method is based on the concept of temporal fuzzy clustering. A fuzzy clustering algorithm is applied to the given part (past+present) of the time series and the calculated clusters and membership matrix are then used to estimate a mixture probability distribution function (PDF) underlying the series. In this way a continuous drift in the series distribution expressed as a drift in the clusters' appearance rate can be estimated. A future PDF can then be predicted by fitting a specific model to the estimated past and future PDF values. This also enables the generation of a minimal-mean-squared-error prediction for a future time series element using the estimated mean value of the predicted PDF.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"2 1","pages":"329-332"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"信号处理","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/SSP.2001.955289","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
We present a method for predicting non-stationary signals generated by a time varying composite source. The method is based on the concept of temporal fuzzy clustering. A fuzzy clustering algorithm is applied to the given part (past+present) of the time series and the calculated clusters and membership matrix are then used to estimate a mixture probability distribution function (PDF) underlying the series. In this way a continuous drift in the series distribution expressed as a drift in the clusters' appearance rate can be estimated. A future PDF can then be predicted by fitting a specific model to the estimated past and future PDF values. This also enables the generation of a minimal-mean-squared-error prediction for a future time series element using the estimated mean value of the predicted PDF.
期刊介绍:
Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.