期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2012
卷号：3
期号：3
页码：731-734
语种：English
出版社：Ayushmaan Technologies
摘要：It has long been known that Dynamic Time Warping (DTW) is superior to Euclidean distance for classification and clustering of time series. However, until lately, most research has utilized Euclidean distance because it is more efficiently calculated. A recently introduced technique that greatly mitigates DTWs demanding CPU time has sparked a flurry of research activity. However, the technique and its many extensions still only allow DTW to be applied to moderately large datasets. In addition, almost all of the research on DTW has focused exclusively on speeding up its calculation; there has been little work done on improving its accuracy. In this work, we target the accuracy aspect of DTW performance and introduce a new framework that learns arbitrary constraints on the warping path of the DTW calculation Apart from improving the accuracy of classification, our technique as a side effect speeds up DTW by a wide margin as well. Along with specified approached kernel functions are used to predict the behavioral patterns of the time series. IN this paper polynomial and sigmoid kernel estimates are used.
关键词：Time series forecasting;combining predictors;regression; ensembles;Kernel estimates;diversity
