Some Improvements for Time Series Subsequence Join based on Pearson Correlation Coefficients

The exact method JOCOR proposed by Mueen et al. is the first method for joining two time series on subsequence correlation. However, this algorithm still has some weaknesses. First, users of JOCOR are required to choose the suitable value of the parameter minLength which is unknown. Second, JOCOR still suffers from high computational cost even for medium-size time series. In this paper, we proposed some novel techniques to improve JOCOR algorithm. These techniques consist of (i) applying some time series segmentation method to divide the outer time series into subsequences, (ii) using a dynamic programming technique in computing the shifted cross product between two time series and (iii) speeding up the subsequence join process by a new way of shifting the sliding window on the outer time series. Extensive experiments have demonstrated that the proposed approach can not only improve the subsequence join in time efficiency but also guarantee the result accuracy.