RESEARCH ON SHAPE-BASED TIME SERIES SIMILARITY MEASURE
The representation and similarity measure of time series are the basis of time series research, and are quite important for improving the efficiency and accuracy of the time series data mining. In this paper, shape-based discrete symbolic representation and distance measure, which is used to measure the similarity between time series are present. This method quantitatively represents the change of the shape of the time series. Compared with the approaches that existing similar, the present method is more intuitive and compact, and is not sensitive to offset translation, amplitude scaling,compress and stretch. That can reflect the degree of the dynamic change of the tendency and erase the influence of the noises, classify the patterns in more detail, which is favorable to improve the accuracy of the clustering, and multi-scale feature. The experimental results show that our approach has good effectiveness in clustering, which can satisfies the requirement of the shape-similarity of time series effectively under various analyzing frequency.
Time series data mining similarity measure representation
XIAO-LI DONG CHENG-KUI GU ZHENG-OU WANG
Institute of Systems Engineering, Tianjin University, Tianjin 300072,China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
1253-1258
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）