Conformal Prediction with Temporal Quantile Adjustments

This is the code associated with "Conformal Prediction with Temporal Quantile Adjustments" (NeurIPS 2022).

To replicate the (GEFCom-R) experiments:

1. Train the base models: python -m utils.main_experiments
2. run main.ipynb for results

For other datasets, please download the corresponding data (see data.preprocessing for more details) and change the __main__ section in utils.main_experiments.

Demo

The demo notebook shows how to use TQA for your own time series model.

Requirements

numpy, torch, pandas, scipy, matplotlib, and tqdm (jupyter and notebook if you want to use the notebook) env.yml contains the full environment.

Update 4/7/2023

I cached and analyzed the results of all my experiments. You'd need persist-to-disk for this (Feel free to use it in your own research!). If you don't need this feature, you can comment out the decorator (@ptd.persistf) in main_experiments.py.

Bibtex

@inproceedings{NEURIPS2022_c8d2860e,
 author = {Lin, Zhen and Trivedi, Shubhendu and Sun, Jimeng},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
 pages = {31017--31030},
 publisher = {Curran Associates, Inc.},
 title = {Conformal Prediction with Temporal Quantile Adjustments},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/c8d2860e1b51a1ffadc7ed0a06f8d8f5-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}