Summary of Janet: Joint Adaptive Prediction-region Estimation For Time-series, by Eshant English et al.
JANET: Joint Adaptive predictioN-region Estimation for Time-series
by Eshant English, Eliot Wong-Toi, Matteo Fontana, Stephan Mandt, Padhraic Smyth, Christoph Lippert
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 framework, JANET (Joint Adaptive predictioN-region Estimation for Time-series), is a novel approach to constructing conformal prediction regions that are valid for both univariate and multivariate time series. It generalizes the inductive conformal framework and efficiently produces joint prediction regions with controlled K-familywise error rates, enabling flexible adaptation to specific application needs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary JANET is a new way to make predictions about future events that’s more reliable and easy to understand. Right now, there are limits on what we can do when making predictions about things like stock prices or weather forecasts over time. The existing methods don’t work well for predicting multiple steps ahead. JANET solves this problem by giving us better control over how much uncertainty is included in the predictions. |
Keywords
* Artificial intelligence * Time series
