{"id":19199869,"url":"https://github.com/rose-stl-lab/copulacpts","last_synced_at":"2025-04-10T11:23:29.007Z","repository":{"id":225005452,"uuid":"760899337","full_name":"Rose-STL-Lab/CopulaCPTS","owner":"Rose-STL-Lab","description":"Code for Copula conformal prediction paper (ICLR 2024)","archived":false,"fork":false,"pushed_at":"2024-09-26T20:20:53.000Z","size":48881,"stargazers_count":27,"open_issues_count":1,"forks_count":4,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-24T10:11:11.536Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Rose-STL-Lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2024-02-20T21:36:47.000Z","updated_at":"2025-03-23T22:12:48.000Z","dependencies_parsed_at":"2024-02-28T20:52:49.617Z","dependency_job_id":null,"html_url":"https://github.com/Rose-STL-Lab/CopulaCPTS","commit_stats":null,"previous_names":["rose-stl-lab/copulacpts"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rose-STL-Lab%2FCopulaCPTS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rose-STL-Lab%2FCopulaCPTS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rose-STL-Lab%2FCopulaCPTS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rose-STL-Lab%2FCopulaCPTS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Rose-STL-Lab","download_url":"https://codeload.github.com/Rose-STL-Lab/CopulaCPTS/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248208606,"owners_count":21065203,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-09T12:29:17.235Z","updated_at":"2025-04-10T11:23:28.987Z","avatar_url":"https://github.com/Rose-STL-Lab.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Copula Conformal Prediction for Multi-step Time Series Forecasting [[Paper](https://arxiv.org/abs/2212.03281)]\n\n\n\n## | Introduction\n\n**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. \n\n## | Citation\n\n[[2212.03281] Copula Conformal Prediction for Multi-step Time Series Forecasting](https://arxiv.org/abs/2212.03281)\n\n```\n@inproceedings{sun2023copula,\n  title={Copula Conformal prediction for multi-step time series prediction},\n  author={Sun, Sophia Huiwen and Yu, Rose},\n  booktitle={The Twelfth International Conference on Learning Representations},\n  year={2023}\n}\n```\n\n## | Installation\n\n\n```bash\npip install -r requirements.txt\n```\n\n## | Datasets\n\nPlease see below for links and refer to Section 5.1 and Appendix C.1 in the paper for processing details. \n\n[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)\n\nThe 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.\n\n\n## | Training and Testing\n\nTo illustrate the usage of our code, we have included pre-generated NRI Particles data in this repository. To replicate the experiment, simply run:\n\n```bash\n./run_experiment.sh\n```\n\n## | Recreate plots in the paper\n\nPlease see ```Visualization.ipynb``` for example code for creating Figure 3 in the paper.\n![fig](fig3.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frose-stl-lab%2Fcopulacpts","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frose-stl-lab%2Fcopulacpts","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frose-stl-lab%2Fcopulacpts/lists"}