Similarity measure based on piecewise linear approximation and derivative dynamic time warping for time series mining

We propose a new method to calculate the similarity of time series based on piecewise linear approximation (PLA) and derivative dynamic time warping (DDTW). The proposed method includes two phases. One is the divisive approach of piecewise linear approximation based on the middle curve of original time series. Apart from the attractive results, it can create line segments to approximate time series faster than conventional linear approximation. Meanwhile, high dimensional space can be reduced into a lower one and the line segments approximating the time series are used to calculate the similarity. In the other phase, we utilize the main idea of DDTW to provide another similarity measure based on the line segments just we got from the first phase. We empirically compare our new approach to other techniques and demonstrate its superiority.

► An efficient divisive approach of PLA is proposed. ► Local and whole trends can be described well by the divisive approach. ► A novel similarity measure is proposed, which improves the results of time series mining.