https://github.com/FilippoMB/Ensemble-Conformalized-Quantile-Regression

Valid and adaptive prediction intervals for probabilistic time series forecasting
https://github.com/FilippoMB/Ensemble-Conformalized-Quantile-Regression

conformal-prediction ensemble-machine-learning frequentist-statistics frequentistic-confidence-intervals prediction-intervals probabilistic-forecasting quantile-regression random-forest recurrent-neural-networks time-series-forecasting time-series-prediction uncertainty-quantification

Last synced: 3 months ago
JSON representation

Valid and adaptive prediction intervals for probabilistic time series forecasting

• Host: GitHub
• URL: https://github.com/FilippoMB/Ensemble-Conformalized-Quantile-Regression
• Owner: FilippoMB
• License: mit
• Created: 2022-02-11T00:19:50.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-28T11:22:36.000Z (about 2 years ago)
• Last Synced: 2024-02-13T08:03:00.593Z (9 months ago)
• Topics: conformal-prediction, ensemble-machine-learning, frequentist-statistics, frequentistic-confidence-intervals, prediction-intervals, probabilistic-forecasting, quantile-regression, random-forest, recurrent-neural-networks, time-series-forecasting, time-series-prediction, uncertainty-quantification
• Language: Jupyter Notebook
• Homepage: https://arxiv.org/abs/2202.08756
• Size: 1.96 MB
• Stars: 66
• Watchers: 2
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-conformal-prediction - Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting

README

        
Python implementation of the ensemble conformalized quantile regression (EnCQR) algorithm, as presented in the original [paper](https://arxiv.org/abs/2202.08756). 

EnCQR allows to generate accurate prediction intervals when predicting a time series with a generic regression algorithm for time series forecasting, such as a Recurrent Neural Network or Random Forest.

---

### Example of usage

The code in [main_EnCQR.py](https://github.com/FilippoMB/Ensemble-Conformalized-Quantile-Regression/blob/main/main_EnCQR.py) shows a quick example of how to perform probabilistic forecasting with EnCQR.

A detailed tutorial can be found in this [notebook](https://nbviewer.org/github/FilippoMB/Ensemble-Conformalized-Quantile-Regression/blob/main/example.ipynb), which explaines how the dataset are preprocessed and shows the differences between different regression models (LSTM, Temporal Convolutional Network, and Random Forest), which can be used as base models in the EnCQR ensemble.

----

### Citation

Consider citing the original paper if you are using EnCQR in your reasearch

	@misc{jensen2022ensemble,

	      title={Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting}, 

	      author={Vilde Jensen and Filippo Maria Bianchi and Stian Norman Anfinsen},

	      year={2022},

	      eprint={2202.08756},

	      archivePrefix={arXiv},

	      primaryClass={cs.LG}

	}