Abstract
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.
| Original language | English |
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| Title of host publication | 2024 IEEE 40th International Conference on Data Engineering Workshops, ICDEW 2024 |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 107-112 |
| Number of pages | 6 |
| ISBN (Electronic) | 979-8-3503-8403-1 |
| DOIs | |
| Publication status | Published - 17 Jun 2024 |
| Event | 40th International Conference on Data Engineering Workshops, ICDEW 2024 - Utrecht, Netherlands Duration: 13 May 2024 → 16 May 2024 |
Conference
| Conference | 40th International Conference on Data Engineering Workshops, ICDEW 2024 |
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| Abbreviated title | ICDEW 2024 |
| Country/Territory | Netherlands |
| City | Utrecht |
| Period | 13/05/24 → 16/05/24 |
Funding
This work has received funding from the European Union s Horizon Europe research and innovation programme STELAR under grant agreement No. 101070122.
| Funders | Funder number |
|---|---|
| European Union's Horizon 2020 - Research and Innovation Framework Programme | 101070122 |
Keywords
- Distance Measures
- Multivariate Time Series