Summary of Quantitative Evaluation Of Motif Sets in Time Series, by Daan Van Wesenbeeck et al.
Quantitative Evaluation of Motif Sets in Time Series
by Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert, Hendrik Blockeel
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel metric for evaluating time series motif discovery (TSMD) is introduced, which addresses limitations of existing metrics. PROM, a broadly applicable metric, is compared to existing ones on TSMD-Bench, a new benchmark for quantitative evaluation of TSMD methods. The results show that PROM provides a more comprehensive evaluation and that the combination of PROM and TSMD-Bench can help understand the relative performance of TSMD methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series motif discovery helps find patterns in data from many fields. This paper makes it easier to compare different ways of doing this by introducing a new way to measure how well they work (PROM) and a new benchmark to test them (TSMD-Bench). This lets us understand which approaches are best. |
Keywords
» Artificial intelligence » Time series
