Summary of Time Series Classification with Random Convolution Kernels: Pooling Operators and Input Representations Matter, by Mouhamadou Mansour Lo et al.
Time series classification with random convolution kernels: pooling operators and input representations matter
by Mouhamadou Mansour Lo, Gildas Morvan, Mathieu Rossi, Fabrice Morganti, David Mercier
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes a novel approach for fast time series classification (TSC), called SelF-Rocket, which builds upon MiniRocket. Unlike existing methods relying on random convolution kernels, SelF-Rocket dynamically selects the best pair of input representations and pooling operators during training. This method achieves state-of-the-art accuracy on the UCR TSC benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SelF-Rocket is a new way to do time series classification quickly. It’s different from other methods because it chooses the best combination of inputs and ways to pool information as it learns. This helps it get better results than before, especially when tested with certain types of data. |
Keywords
» Artificial intelligence » Classification » Time series
