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With the development of information science and the study of uncertainty, the research of traditional time series has met new challenges. This paper chooses the similarity computation of uncertain time series as research target. Firstly, the uncertainty of the time series is studied systematically, and the reasons for the uncertainty are discussed. Then, based on kernel density estimation, the author attempts to put forward an algorithm for computing the similarity of uncertain time series, which is essentially a sequential pattern discovery and matching process. The density of the uncertain time series is estimated, and then the density function is clustered by using the mining method. By then the uncertain time series is transformed into a state sequence, and the similarity between sequences is calculated by a matching method. Finally, this approach we proposed and other uncertain time similarity calculation method were compared. The results show that this method has better performance, density distribution in reality has a certain regularity, but worse than completely random density distribution.

A Histogram Kernel Density Estimation Based Similarity Approach for Uncertain Time Series