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Conformal predictions could be a valuable addition to darts.
It would require some brain storming/planning of how (or if) we can integrate this into our API / extend the API.
Some links:
- htt…
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### 🚀 The feature, motivation and pitch
I propose the addition of a conformal prediction framework to the PyTorch library. This framework would include the implementation of split conformal predictio…
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A new SOTA method paper https://arxiv.org/abs/2407.04407
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### Description
It would be great to have Conformal Prediction in NeuralForecast, similar to statsforecast and mlforecast.
### Use case
_No response_
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**Is your feature request related to a problem? Please describe.**
Conformal Prediction is a powerful uncertainty quantification framework that can benefit multiple stages of Twitter algorithm
**D…
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Problem: CatBoost is a great library, but it currently lacks reliable modern uncertainty quantification that is rather easy to implement using conformal prediction. https://github.com/valeman/awesome-…
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**Is your feature request related to a problem? Please describe.**
> Despite attractive theoretical guarantees and practical successes, Predictive Interval (PI) given by Conformal Prediction (CP) m…
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I'm excited by the prospect of uncertainty quantification via conformal prediction that has been implemented. I noticed that it can do quantiles and prediction intervals in the current state. Would it…
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Problem: XGBoost is a great library, but it currently lacks reliable modern uncertainty quantification that is rather easy to implement using conformal prediction. https://github.com/valeman/awesome-…
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Hi @Jianguo99,
I am new to conformal prediction, and I have a multitask, multi-output model that performs both classification and regression for a specific problem. Is it possible to use this kind …