Summary of Right on Time: Revising Time Series Models by Constraining Their Explanations, By Maurice Kraus et al.
Right on Time: Revising Time Series Models by Constraining their Explanations
by Maurice Kraus, David Steinmann, Antonia Wüst, Andre Kokozinski, Kristian Kersting
First submitted to arxiv on: 20 Feb 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 introduces Right on Time (RioT), a method to mitigate confounding factors in deep time series models, which often rely on such factors to produce incorrect outputs. The new dataset, P2S, from a mechanical production line highlights the issue. RioT enables interactions with model explanations across both the time and frequency domains, using feedback to constrain the model and steer it away from annotated confounders. This approach is crucial for addressing confounders in time series datasets. Empirical results show that RioT effectively guides models away from wrong reasons in P2S and popular classification and forecasting datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us make better predictions by fixing a problem with deep learning models. These models often get confused because they rely on things that don’t matter, like noise or random patterns. To solve this, the researchers created a new dataset called P2S from a real factory. They also developed a method called RioT that lets you see what’s going wrong and use that information to correct the model. This helps the model focus on the important features and avoid mistakes. The results show that RioT works well in different types of datasets. |
Keywords
* Artificial intelligence * Classification * Deep learning * Time series
