Abstract
SSSD is an open-source Data Science project. Repository for the paper: 'Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models'. This is the official repository for the paper Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models) accepted by TMLR . In combination with (conditional) diffusion and state-space models, we put forward diverse algorithms, particualary, we propose the generative model $SSSD^{S4}$, which is suited to capture long-term dependencies and demonstrates state-of-the-art results in time series across diverse missing scenarios and datasets. It is built using Python, Deep Learning. 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
This is the official repository for the paper Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models) accepted by TMLR . In combination with (conditional) diffusion and state-space models, we put forward diverse algorithms, particualary, we propose the generative model $SSSD^{S4}$, which is suited to capture long-term dependencies and demonstrates state-of-the-art results in time series across diverse missing scenarios and datasets.
Visit the source directory to get datasets download and experiments reproducibility instructions. (here is an example of the feature sampling approach for the datasets with large number of channels )
2. Objective
Repository for the paper: 'Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models'
This project demonstrates how Python, Deep Learning 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/AI4HealthUOL/SSSD.git
cd SSSDFull 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 AI4HealthUOL 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.
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