Ensemble Conformalized Quantile Regression

Valid and adaptive prediction intervals for probabilistic time series forecasting.

Data ScienceJupyter NotebookMIT

Abstract

Ensemble Conformalized Quantile Regression is an open-source Data Science project. Valid and adaptive prediction intervals for probabilistic time series forecasting. Python implementation of the ensemble conformalized quantile regression (EnCQR) algorithm, as presented in the paper Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting by V. Jensen, F. It is built using Jupyter Notebook. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.

1. Introduction

Python implementation of the ensemble conformalized quantile regression (EnCQR) algorithm, as presented in the paper Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting by V. Jensen, F. M. Bianchi and S. N. Anfinsen.

EnCQR is a post-hoc method for uncertainty quantification. It creates valid prediction intervals on top of a generic regression algorithm for time series forecastings, such as a Recurrent Neural Network, ARIMA, Random Forest, and so on.

2. Objective

Valid and adaptive prediction intervals for probabilistic time series forecasting.

This project demonstrates how Jupyter Notebook can be applied to a real-world Data Science problem.

4. Technology Stack

Jupyter Notebook

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
  • pip for dependencies
  • Git (to clone the repository)

6. Installation & Setup

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

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Turn the analysis into an interactive dashboard
  • Automate data refresh with a scheduled job
  • Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What is the source of the dataset and how was missing data handled?
  2. Which exploratory analysis steps revealed the most useful insight?
  3. Why were these particular charts chosen to present the data?
  4. Which statistical or ML technique supports the conclusions?
  5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by FilippoMB and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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