Conformal Quantile Regression

[saattrupdan]

Conformal Quantile Regression was introduced in Romano, Patterson & Candès and is a variant of quantile regression which calibrates the prediction intervals, yielding narrower intervals, while preserving theoretical coverage guarantees.

This could potentially be built into QuantileLinearRegression via a conformal argument.

[valeman]

Dan, you might be interested in this link

https://github.com/valeman/awesome-conformal-prediction

[leandroohf]

Not sure if It is the right place for this message.

Since you mentioned Future work, you want to add support for neural networks. I would like to recommend looking into this paper: High-Quality Prediction Interval.

• https://arxiv.org/pdf/1802.07167.pdf
• https://github.com/TeaPearce/Deep_Learning_Prediction_Intervals

I started this notebook (WIP): https://github.com/leandroohf/machine_learning_algorithms/blob/master/dev/intro_to_prediction_interval.ipynb

and while doing my research, I discovered your packet doubt and decided to try.