Abstract
PDF is an open-source Data Science project. Official implementation of "Periodicity Decoupling Framework for Long-term Series Forecasting" (ICLR 2024). Quantitatively, compared with Transformer-based models, PDF(720) yields an overall 14.59% reduction in MSE and 10.77% reduction in MAE. Compared with CNN-based models, PDF(720) yields an overall 24.61% reduction in MSE and 19.91% reduction in MAE. It is built using Python. The complete source code is publicly available on GitHub under the GNU Affero General Public License v3.0, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
Quantitatively, compared with Transformer-based models, PDF(720) yields an overall 14.59% reduction in MSE and 10.77% reduction in MAE. Compared with CNN-based models, PDF(720) yields an overall 24.61% reduction in MSE and 19.91% reduction in MAE. Compared with Linear-based models, PDF(720) yields an overall 7.05% reduction in MSE and 5.51% reduction in MAE.
2. Objective
Official implementation of "Periodicity Decoupling Framework for Long-term Series Forecasting" (ICLR 2024)
This project demonstrates how Python can be applied to a real-world Data Science problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later
- pip / virtualenv for dependencies
- VS Code, PyCharm or Jupyter Notebook
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/Hank0626/PDF.git
cd PDFFull 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.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by Hank0626 and published on GitHub under the GNU Affero General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.
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