Adaptive Feature Based Dynamic Time Warping

Dynamic time warping (DTW) has been widely used in various pattern recognition and time series data mining applications. However, as examples will illustrate, both the classic DTW and its later alternative, derivative DTW, may fail to align a pair of sequences on their common trends or patterns. Furthermore, the learning capability of any supervised learning algorithm based on classic/derivative DTW is very limited. In order to capture trends or patterns that a sequence presents during the alignment process, we first derive a global feature and a local feature for each point in a sequence. Then, a method called feature based dynamic time warping (FBDTW) is designed to align two sequences based on each point‟s local and global features instead of its value or derivative. Experimental study shows that FDBTW outperforms both classic DTW and derivative DTW on pairwise distance evaluation of time series sequences. In order to enhance the capacity of supervised learning based on DTW, we further design a method called adaptive feature based dynamic time warping (AFDBTW) by equipping the FDBTW with a novel feature selection algorithm. This feature selection algorithm is able to expand the learning capability of any DTW based supervised learning algorithm by a dual learning process. The first-fold learning process learns the significances of both the local feature and global feature towards classification; then the second-fold learning process learns a classification model based on the pairwise distances generated by the AFDBTW. A comprehensive experimental study shows that the AFDBTW is able to make further improvement over the FDBTW in time series classification.