Correlation based dynamic time warping of multivariate time series

Combines DTW and PCA based similarity measures.

A novel algorithm called correlation based dynamic time warping (CBDTW) wich combines DTW and PCA based similarity measures. To preserve correlation, multivariate time series are segmented and the local dissimilarity function of DTW originated from SPCA. The segments are obtained by bottom-up segmentation using special, PCA related costs. Our novel technique qualitified on two databases, the database of signature verification competition 2004 and the commonly used AUSLAN dataset. We show that CBDTW outperforms the standard SPCA and the most commonly used, Euclidean distance based multivariate DTW in case of datasets wich complex correlation structure.

The algorithm is also described in:
J. Abonyi, F. Szeifert, Supervised fuzzy clustering for the identification of fuzzy classifiers, Pattern Recognition Letters, 24(14) 2195-2207, October 2003