Nearest centroid classifier for multivariate time series using DTW-MP centroid

This research presents results from evaluating a range of centroid functions for the nearest centroid classifier applied to several multivariate time series classification datasets. These are tested on seven of the largest benchmark datasets available. The centroid functions includes two proposed methods for generating centroids from time series: DTW-MP I and DTW-MP D and five state-of-art centroid functions: SoftDTW, DBA, PAM, Mean and SE. These proposed centroid algorithms use dynamic time warping to combine any number of time series together into one representative centroid. The input time series can be of different lengths and of multivariate data (n-dimensional). Centroids of time series have a wide range of uses but are often used as a nearest centroid classifier which is faster than a DTW-1NN classifier due to a much lower number of DTW comparisons being required to classify a new time series into existing classes. Therefore we utilize the task of nearest centroid classification in this research as a means to evaluate the performance of two proposed and five state-of-art centroid functions. For evaluation, six of the largest publicly available multivariate time series benchmark datasets currently available were used. These results show that the proposed DTW-MP I and DTW-MP D centroid algorithms perform comparatively with state-of-art time series centroid functions and these could have further uses in other time series applications.