AI4HealthUOL/SSSD
Repository for the paper: 'Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models'
This project helps operations engineers, data scientists, or researchers who work with time-series data to accurately fill in missing data points and predict future trends. You input your existing time-series data, even if it has gaps or inconsistencies, and it outputs a more complete dataset and forecasts for future values. This is designed for professionals analyzing complex, long-term sequential data.
333 stars. No commits in the last 6 months.
Use this if you need to reliably complete incomplete time-series data and make accurate long-term predictions, especially when dealing with various types of missing information.
Not ideal if your primary need is for simple, short-term forecasting without significant missing data challenges.
Stars
333
Forks
58
Language
Python
License
MIT
Category
Last pushed
May 24, 2025
Commits (30d)
0
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