期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2015
卷号：7
期号：3
出版社：International Center for Scientific Research and Studies
摘要：The operation of time series analysis to effectively manage the large amounts of data with high dimensional became an important research problem. Choose effective and scalable algorithms for appropriate representation of data is another challenge. A lot of high-level representations of the time series have been proposed for data extraction, such as spectral transfers, wavelets, piecewise polynomial, symbolic models, etc. One of the methods is Piecewise Aggregate Approximation (PAA) which minimizes dimensionality by the mean values of equal-sized frames, but this focus on mean value takes into consideration only the central tendency and not the dispersion present in each segment, which may lead to some important patterns being missed in some time series data sets. We propose method based on Time-Weighted Average for Symbolic Aggregate approximation method (TWA_SAX) compare its performance with some current methods. TWA_SAX is enables raw data to be specifically compared to the minimized representation and, at the same time, ensures reduced limits to Euclidean distance. It can be utilized to generate quicker, more precise algorithms for similarity searches, which improves the preciseness of time series representation through enabling better tightness of the lower bound
