Abstract: To address the issues on the over expensive time cost,an incremental dynamic time warping (IDTW) to measure the similarity between two time series was proposed．First of all,dynamic time warping (DTW) was used to measure similarity of the past time sequences and retrieves the best warping path and the cumulated distance cost of each element in the warping path．Next,after computing the similarity between the two current time series by backward warping method,a new warping path intersects with the past one was obtained and its warping distance was minimal．Finally,the incremental dynamic warping method was realized to measure similarity．The new method not only has the good quality to measure the similarity but also is efficient to compute．The numerical experiments demonstrate that the classification accuracy and computing performance of IDTW are better than DTW.

Key words: Time series data mining,Dynamic time warping,Incremental dynamic time warping,Similarity measure