Summary of Forest Proximities For Time Series, by Ben Shaw et al.
Forest Proximities for Time Series
by Ben Shaw, Jake Rhodes, Soukaina Filali Boubrahimi, Kevin R. Moon
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 This paper introduces PF-GAP, an extension of RF-GAP proximity measure to proximity forests, a reliable and efficient time series classification model. By using forest proximities with Multi-Dimensional Scaling, the authors generate vector embeddings for univariate time series, comparing them to those obtained from various distance measures. Additionally, they investigate the connection between misclassified points and outliers by combining forest proximities with Local Outlier Factors, contrasting this approach with nearest neighbor classifiers that employ traditional time series distance measures. The results suggest that forest proximities may exhibit a stronger link between misclassified points and outliers compared to nearest neighbor classifiers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving ways to classify time series data, which are sequences of numbers over time. They introduce a new method called PF-GAP, which combines two existing methods: RF-GAP and proximity forests. This allows them to generate better representations of the data using something called Multi-Dimensional Scaling. The authors also explore how misclassified points relate to outliers in the data, comparing their approach to another common technique used for this purpose. Overall, the results show that PF-GAP can be a useful tool for understanding and classifying time series data. |
Keywords
» Artificial intelligence » Classification » Nearest neighbor » Time series
