Summary of Lets-c: Leveraging Language Embedding For Time Series Classification, by Rachneet Kaur et al.
LETS-C: Leveraging Language Embedding for Time Series Classification
by Rachneet Kaur, Zhen Zeng, Tucker Balch, Manuela Veloso
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Methodology (stat.ME)
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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 proposed approach utilizes a language embedding model to embed time series and pairs the embeddings with a simple classification head composed of CNN and MLP. The method, called LETS-C, outperforms current SOTA in classification accuracy and uses significantly fewer trainable parameters. This lightweight solution has promising implications for achieving high-performance time series classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Instead of using large language models to classify time series data, this paper proposes a different approach that combines language embedding with CNN and MLP. The result is a model called LETS-C that performs better than the current best methods while requiring fewer calculations. This makes it more efficient and could be useful for real-world applications. |
Keywords
* Artificial intelligence * Classification * Cnn * Embedding * Time series
