Similarity Measure Based on Incremental Warping Window for Time Series Data Mining

A similarity measure is one of the most important tasks in the fields of time series data mining. Its quality often affects the efficiency and effectiveness of the related algorithms that need to measure the similarity between two time series in advance. Dynamic time warping is one of the most robust methods to compare one time series with another based on warping alignments. In this paper, the design of an incremental warping window is used to improve the performance of dynamic time warping. The incremental warping window is changeable for various time series with different lengths. Moreover, the improved dynamic time warping based on the novel window considers the recent alignments as much as possible, which indicates that the proposed method concentrates on more information of the recent data points than that of the previous data points. In addition, it is suitable for online similarity measure between data stream. The experimental evaluation shows that the proposed method is effective and efficient for time series mining.