Summary of Measuring Pre-training Data Quality Without Labels For Time Series Foundation Models, by Songkang Wen et al.
Measuring Pre-training Data Quality without Labels for Time Series Foundation Models
by Songkang Wen, Vasilii Feofanov, Jianfeng Zhang
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 investigates the performance of a contrastive-learning-based foundation model in time series classification, focusing on the role of pre-training data diversity. The researchers introduce a new measure, contrastive accuracy, to evaluate the quality of the representation space learned by the model. Their experiments demonstrate a positive correlation between contrastive accuracy and downstream task accuracy, suggesting that the proposed metric can guide the search for optimal pre-training datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at special kinds of computer models called foundation models that can be used for many different tasks. One important part is making sure they’re trained on lots of different data beforehand. For time series data, like stock prices or weather forecasts, it’s hard to find enough training data. The researchers test a new kind of model that uses a special method called contrastive learning. They also create a new way to measure how well the model is doing, which they call contrastive accuracy. They found that if the model does better on this measurement, it will also do better at other tasks. This means that scientists can use this new metric to find the best training data for their models. |
Keywords
» Artificial intelligence » Classification » Time series
