Summary of Transformer Conformal Prediction For Time Series, by Junghwan Lee et al.
Transformer Conformal Prediction for Time Series
by Junghwan Lee, Chen Xu, Yao Xie
First submitted to arxiv on: 8 Jun 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 A novel conformal prediction method for time series forecasting is introduced, leveraging the Transformer architecture to capture long-term dependencies. The approach employs the Transformer decoder as a conditional quantile estimator to predict the quantiles of prediction residuals, which are then used to estimate the prediction interval. By learning temporal dependencies across past prediction residuals, the proposed method aims to improve the estimation of the prediction interval. Experimental results on simulated and real data demonstrate the superiority of this approach compared to existing state-of-the-art conformal prediction methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way to predict future values in time series data using the Transformer machine learning model. The goal is to make more accurate predictions by understanding how past errors are connected over time. This method works well on both fake and real-world data, showing that it’s better than other approaches in this area. |
Keywords
» Artificial intelligence » Decoder » Machine learning » Time series » Transformer
