Abstract: The central time series plays an important role in time series clustering,which indicates the common features of time series.We proposed dynamic planning approach called DPSSD to calculate central time series of two time series.The approach is recursive based on the minimizing sum of squares of DTW (SSD) distance from central series to two sample series.Degree-pruning was also introduced to decrease the algorithm time complexity.The proposed algorithm was proved theoretically.It can achieve the optimal solution.In the experiments,the results illustrate that our approaches have much better performance and robustness than DBA,which is measured by SSD.

Key words: Central time series,DTW,Dynamic planning,Degree-pruning