Similarity measure based on partial information of time series

Similarity measure of time series is an important subroutine in many KDD applications. Previous similarity models mainly focus on the prominent series behaviors by considering the whole information of time series. In this paper, we address the problem: which portion of information is more suitable for similarity measure for the data collected from a certain field. We propose a model for the retrieval and representation of the partial information in time series data, and a methodology for evaluating the similarity measurements based on partial information. The methodology is to retrieve various portions of information from the raw data and represent it in a concise form, then cluster the time series using the partial information and evaluate the similarity measurements through comparing the results with a standard classification. Experiments on data set from stock market give some interesting observations and justify the usefulness of our approach.