Multivariate time series classification with parametric derivative dynamic time warping

• We improve DTW distance measure in multivariate time series classification.
• We use derivatives to improve DTW in multivariate time series classification.
• We test effectiveness on 18 real time series.
• We present a detailed comparison of proposed methods.

Multivariate time series (MTS) data are widely used in a very broad range of fields, including medicine, finance, multimedia and engineering. In this paper a new approach for MTS classification, using a parametric derivative dynamic time warping distance, is proposed. Our approach combines two distances: the DTW distance between MTS and the DTW distance between derivatives of MTS. The new distance is used in classification with the nearest neighbor rule. Experimental results performed on 18 data sets demonstrate the effectiveness of the proposed approach for MTS classification.