# RDPI: A Refine Diffusion Probability Generation Method for Spatiotemporal Data Imputation

arXiv: https://arxiv.org/abs/2412.12642 · HTML: https://mlsanity.com/paper/2412.12642/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 2 of its 37 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 2 of 2)

- [Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models](https://mlsanity.com/paper/2208.09399/) (2022)
- [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://mlsanity.com/paper/1707.01926/) (2017)

## Built on by (most cited first, 4 of 4)

- [Step-Aware Residual-Guided Diffusion for EEG Spatial Super-Resolution](https://mlsanity.com/paper/2510.19166/) (2025)
- [Geometry- and Relation-Aware Diffusion for EEG Super-Resolution](https://mlsanity.com/paper/2602.02238/) (2026)
- [EMAG: Differentiable 4D Gaussian Mixture Splatting for EEG Spatial Super-Resolution](https://mlsanity.com/paper/2605.29731/) (2026)
- [TGSD: Topology-Guided State-Space Diffusion Framework for EEG Spatial Super-Resolution](https://mlsanity.com/paper/2606.03998/) (2026)

## Abstract (arXiv)

Spatiotemporal data imputation plays a crucial role in various fields such as traffic flow monitoring, air quality assessment, and climate prediction. However, spatiotemporal data collected by sensors often suffer from temporal incompleteness, and the sparse and uneven distribution of sensors leads to missing data in the spatial dimension. Among existing methods, autoregressive approaches are prone to error accumulation, while simple conditional diffusion models fail to adequately capture the spatiotemporal relationships between observed and missing data. To address these issues, we propose a novel two-stage Refined Diffusion Probability Impuation (RDPI) framework based on an initial network and a conditional diffusion model. In the initial stage, deterministic imputation methods are used to generate preliminary estimates of the missing data. In the refinement stage, residuals are treated as the diffusion target, and observed values are innovatively incorporated into the forward process. This results in a conditional diffusion model better suited for spatiotemporal data imputation, bridging the gap between the preliminary estimates and the true values. Experiments on multiple datasets demonstrate that RDPI not only achieves state-of-the-art imputation accuracy but also significantly reduces sampling computational costs.