Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.

Related tags

Deep Learning conformal-rnn
Overview

Conformal time-series forecasting

Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.

If you use our code in your research, please cite:

@inproceedings{stankeviciute2021conformal,
  author = {Stankevičiūtė, Kamilė and Alaa, Ahmed M. and {van der Schaar}, Mihaela},
  title = {Conformal time-series forecasting},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2021}
}

This codebase builds on the implementation for "Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions" (ICML 2020), available at https://github.com/ahmedmalaa/rnn-blockwise-jackknife under the BSD 3-clause license.

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Comments
  • Question on unintuitive results for Bonferroni correction when alpha = 0.5

    Question on unintuitive results for Bonferroni correction when alpha = 0.5

    I have been involved in implementing a conformal interval wrapper based on this method in sktime.

    I am confused about how the Bonferroni correction works and wondered if you could help. There are more details in this issue and this PR but essentially I think the implementation is as in the paper but the results when alpha=0.5 are unintuitive (the values get much larger and it throws the forecasts off).

    We don't use the factor of (n_calibration + 1.0)/n_calibration as you do in this code but that is close to 1 so I don't think it would make the difference. Do you have any pointers to what could be going wrong here?

    Thanks

    opened by bethrice44 0
Owner
Kamilė Stankevičiūtė
ML for Medicine PhD student @vanderschaarlab, University of Cambridge. Oxford MSc CS, ex-Google.
Kamilė Stankevičiūtė
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