https://github.com/rose-stl-lab/copulacpts
Code for Copula conformal prediction paper (ICLR 2024)
https://github.com/rose-stl-lab/copulacpts
Last synced: about 1 year ago
JSON representation
Code for Copula conformal prediction paper (ICLR 2024)
- Host: GitHub
- URL: https://github.com/rose-stl-lab/copulacpts
- Owner: Rose-STL-Lab
- License: mit
- Created: 2024-02-20T21:36:47.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-26T20:20:53.000Z (over 1 year ago)
- Last Synced: 2025-03-24T10:11:11.536Z (about 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 46.6 MB
- Stars: 27
- Watchers: 2
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Copula Conformal Prediction for Multi-step Time Series Forecasting [[Paper](https://arxiv.org/abs/2212.03281)]
## | Introduction
**Copula** **C**onformal **P**rediction algorithm for multivariate, multi-step **T**ime **S**eries (CopulaCPTS) is a conformal prediction algorithm with full-horizon validity guarantee.
## | Citation
[[2212.03281] Copula Conformal Prediction for Multi-step Time Series Forecasting](https://arxiv.org/abs/2212.03281)
```
@inproceedings{sun2023copula,
title={Copula Conformal prediction for multi-step time series prediction},
author={Sun, Sophia Huiwen and Yu, Rose},
booktitle={The Twelfth International Conference on Learning Representations},
year={2023}
}
```
## | Installation
```bash
pip install -r requirements.txt
```
## | Datasets
Please see below for links and refer to Section 5.1 and Appendix C.1 in the paper for processing details.
[Particles](https://github.com/mitmul/chainer-nri) | [Drone](https://github.com/AtsushiSakai/PythonRobotics)| [Epidemiology](https://coronavirus.data.gov.uk/details/download) | [Argoverse 1](https://www.argoverse.org/av1.html)
The processed files for Particles, Drone, and Epidemiology datasets are located in the `./data` directory. If you want to reporduce the visualizations, you might need to refer to the original sources for metadata.
## | Training and Testing
To illustrate the usage of our code, we have included pre-generated NRI Particles data in this repository. To replicate the experiment, simply run:
```bash
./run_experiment.sh
```
## | Recreate plots in the paper
Please see ```Visualization.ipynb``` for example code for creating Figure 3 in the paper.
