Beyond the Dimensions: A Structured Evaluation of Multivariate Time Series Distance Measures
Published in ICDE 2024 Workshops (MulTiSA), 2024
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.
This workshop paper was the starting point for our much larger SIGMOD 2025 study of 30 measures. I presented it at the MulTiSA workshop in Utrecht.
Recommended citation: J. E. d'Hondt, O. Papapetrou and J. Paparrizos, "Beyond the Dimensions: A Structured Evaluation of Multivariate Time Series Distance Measures," 2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW), Utrecht, Netherlands, 2024, pp. 107–112.
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