Averaging Methods using Dynamic Time Warping for Time Series Classification

Abstract

Averaging is an important step in time series classi-fication or clustering, to create representative sequences for each category of data. A global averaging method for Dynamic Time Warping (DTW) based time series analysis is DTW Barycenter Averaging (DBA). In this paper, we propose a recursive tree based implementation of DBA, for faster computation of an average sequence, using the divide-and-conquer strategy. We also propose to automate the termination of DBA using a data-driven approach. The performance of the proposed methods is evaluated using accuracy, precision and recall as performance metrics, in a simple DTW-distance based classification method on ten standard time series datasets. Experimental results demonstrate that the proposed approaches are significantly faster than DBA, while achieving similar performance.