A variety of distance measures for multivariate time series has been proposed in recent literature. However, evaluations of such measures have been incomplete; comparisons are limited to subsets of similar measures, lacking a holistic view of the field with an appropriate taxonomy of measures. This paper presents a structured evaluation of multivariate time series distance measures. Through a novel taxonomy, measures are categorized based on how they handle the multiple variates; in an atomic or a holistic manner. Experimental evaluation of 12 measures shows that no single measure or approach is superior; the optimal choice depends on the data and the task at hand.
No takes yet. Share an insight, caveat, or question.
d’Hondt et al. (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: