Research on Shape-Based Time Series Similarity Measure
基于形态的时间序列相似性度量研究

The representation and similarity measure of time series are the basis of time series research, which is quite important to improving the efficiency and accuracy of the time series data mining. This paper proposes a shape-based discrete symbolic representation and its corresponding distance measure to measure the similarity between time series. The present method is intuitive and compact, and not sensitive to the shifting, amplitude scaling, compression and stretch of data. The method can reflect the degree of the dynamic change of the tendency and erase the influence of the noises, and it has multi-scale characterization. The experimental results show that the approach has good effect in clustering, which can measure the shape-similarity of time series effectively under various analyzing frequency.