Summary of Conformalised Conditional Normalising Flows For Joint Prediction Regions in Time Series, by Eshant English et al.
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series
by Eshant English, Christoph Lippert
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 In this paper, researchers develop a method to apply Conformal Prediction to probabilistic generative models like Normalising Flows, specifically for multi-step time series forecasting. The proposed approach leverages the flexibility of Normalising Flows to generate potentially disjoint prediction regions, leading to improved predictive efficiency in the presence of multimodal predictive distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to predict uncertain outcomes using machine learning models. It’s like getting a range of possible answers instead of just one answer. The method works by combining two things: Normalising Flows and Conformal Prediction. This helps to make predictions more accurate, especially when there are multiple possible outcomes. |
Keywords
» Artificial intelligence » Machine learning » Time series
