Revisiting Inaccuracies of Time Series Averaging under Dynamic Time Warping

09/07/2018
by   Brijnesh Jain, et al.
0

This article revisits an analysis on inaccuracies of time series averaging under dynamic time warping conducted by Niennattrakul2007. The authors presented a correctness-criterion and introduced drift-outs of averages from clusters. They claimed that averages are inaccurate if they are incorrect or drift-outs. Furthermore, they conjectured that such inaccuracies are caused by the lack of triangle inequality. We show that a rectified version of the correctness-criterion is unsatisfiable and that the concept of drift-out is geometrically and operationally inconclusive. Satisfying the triangle inequality is insufficient to achieve correctness and unnecessary to overcome the drift-out phenomenon. We place the concept of drift-out on a principled basis and show that sample means as global minimizers of a Fréchet function never drift out. The adjusted drift-out is a way to test to which extent an approximation is coherent. Empirical results show that solutions obtained by the state-of-the-art methods SSG and DBA are incoherent approximations of a sample mean in over a third of all trials.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset