AdaSTI: Conditional Diffusion Models with Adaptive Dependency Modeling for Spatio-Temporal Imputation

• URL: http://arxiv.org/abs/2509.18144v1
• Date: Mon, 15 Sep 2025 18:55:56 GMT
• Title: AdaSTI: Conditional Diffusion Models with Adaptive Dependency Modeling for Spatio-Temporal Imputation
• Authors: Yubo Yang, Yichen Zhu, Bo Jiang,
• Abstract summary: We propose Ada, a novel S-temporal imputation approach based on conditional diffusion model.<n>Ada outperforms existing methods in all the settings, with up to 46.4% reduction in imputation error.
• Score: 18.411685240380333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatio-temporal data abounds in domain like traffic and environmental monitoring. However, it often suffers from missing values due to sensor malfunctions, transmission failures, etc. Recent years have seen continued efforts to improve spatio-temporal data imputation performance. Recently diffusion models have outperformed other approaches in various tasks, including spatio-temporal imputation, showing competitive performance. Extracting and utilizing spatio-temporal dependencies as conditional information is vital in diffusion-based methods. However, previous methods introduce error accumulation in this process and ignore the variability of the dependencies in the noisy data at different diffusion steps. In this paper, we propose AdaSTI (Adaptive Dependency Model in Diffusion-based Spatio-Temporal Imputation), a novel spatio-temporal imputation approach based on conditional diffusion model. Inside AdaSTI, we propose a BiS4PI network based on a bi-directional S4 model for pre-imputation with the imputed result used to extract conditional information by our designed Spatio-Temporal Conditionalizer (STC)network. We also propose a Noise-Aware Spatio-Temporal (NAST) network with a gated attention mechanism to capture the variant dependencies across diffusion steps. Extensive experiments on three real-world datasets show that AdaSTI outperforms existing methods in all the settings, with up to 46.4% reduction in imputation error.

Related papers

• Which Layer Causes Distribution Deviation? Entropy-Guided Adaptive Pruning for Diffusion and Flow Models [77.55829017952728]
  EntPruner is an entropy-guided automatic progressive pruning framework for diffusion and flow models.<n>Experiments on DiT and SiT models demonstrate the effectiveness of EntPruner, achieving up to 2.22$times$ inference speedup.
  arXiv  Detail & Related papers  (2025-11-26T07:20:48Z)
• DS-Diffusion: Data Style-Guided Diffusion Model for Time-Series Generation [4.14524872656857]
  We propose a data style-guided diffusion model (DS-Diffusion) for time series generation tasks.<n>The DS-Diffusion avoids retraining the entire framework to introduce conditional guidance.<n>The generated samples can clearly indicate the data style from which they originate.
  arXiv  Detail & Related papers  (2025-09-23T03:06:39Z)
• A theoretical framework for self-supervised contrastive learning for continuous dependent data [79.62732169706054]
  Self-supervised learning (SSL) has emerged as a powerful approach to learning representations, particularly in the field of computer vision.<n>We propose a novel theoretical framework for contrastive SSL tailored to emphsemantic independence between samples.<n>Specifically, we outperform TS2Vec on the standard UEA and UCR benchmarks, with accuracy improvements of $4.17$% and $2.08$%, respectively.
  arXiv  Detail & Related papers  (2025-06-11T14:23:47Z)
• Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion [7.021277706390712]
  Missing intemporal systems presents a challenge for modern applications ranging from environmental monitoring to urban traffic management.<n>Current approaches based on machine learning and deep learning struggle to model thedependencies between spatial and temporal dimensions effectively.<n>CoFILL builds on the inherent advantages of diffusion-quality models to generate high-quality imputations.
  arXiv  Detail & Related papers  (2025-06-08T11:53:06Z)
• Continuous Diffusion Model for Language Modeling [57.396578974401734]
  Existing continuous diffusion models for discrete data have limited performance compared to discrete approaches.<n>We propose a continuous diffusion model for language modeling that incorporates the geometry of the underlying categorical distribution.
  arXiv  Detail & Related papers  (2025-02-17T08:54:29Z)
• RDPI: A Refine Diffusion Probability Generation Method for Spatiotemporal Data Imputation [4.251739849724956]
  imputation plays a crucial role in various fields such as traffic flow monitoring, air quality assessment and climate prediction.<n>Data collected by sensors often suffer from temporal incompleteness, and the accumulation and uneven distribution leads to missing data.<n>We propose a novel two-stage refined probability imputation framework based on an initial network and a conditional diffusion model.
  arXiv  Detail & Related papers  (2024-12-17T08:06:00Z)
• Amortized Posterior Sampling with Diffusion Prior Distillation [55.03585818289934]
  Amortized Posterior Sampling is a novel variational inference approach for efficient posterior sampling in inverse problems.<n>Our method trains a conditional flow model to minimize the divergence between the variational distribution and the posterior distribution implicitly defined by the diffusion model.<n>Unlike existing methods, our approach is unsupervised, requires no paired training data, and is applicable to both Euclidean and non-Euclidean domains.
  arXiv  Detail & Related papers  (2024-07-25T09:53:12Z)
• Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data [55.54827581105283]
  We show that the concrete score in absorbing diffusion can be expressed as conditional probabilities of clean data.<n>We propose a dedicated diffusion model without time-condition that characterizes the time-independent conditional probabilities.<n>Our models achieve SOTA performance among diffusion models on 5 zero-shot language modeling benchmarks.
  arXiv  Detail & Related papers  (2024-06-06T04:22:11Z)
• A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation [35.46631415365955]
  We introduce a conditional diffusion framework called C$2$TSD, which incorporates disentangled temporal (trend and seasonality) representations as conditional information. Our experiments on three real-world datasets demonstrate the superior performance of our approach compared to a number of state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-18T11:59:04Z)
• Erasing Undesirable Influence in Diffusion Models [51.225365010401006]
  Diffusion models are highly effective at generating high-quality images but pose risks, such as the unintentional generation of NSFW (not safe for work) content. In this work, we introduce EraseDiff, an algorithm designed to preserve the utility of the diffusion model on retained data while removing the unwanted information associated with the data to be forgotten.
  arXiv  Detail & Related papers  (2024-01-11T09:30:36Z)
• PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation [35.62945607302276]
  We propose a conditional diffusion framework for Stemporal imputation with the prior modeling, named PriSTI. PriSTI outperforms existing imputation methods in various missing patterns of different real-world data, and effectively handles scenarios such as high missing rates and sensor failure.
  arXiv  Detail & Related papers  (2023-02-20T03:52:53Z)
• CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [107.63407690972139]
  Conditional Score-based Diffusion models for Imputation (CSDI) is a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data. CSDI improves by 40-70% over existing probabilistic imputation methods on popular performance metrics. In addition, C reduces the error by 5-20% compared to the state-of-the-art deterministic imputation methods.
  arXiv  Detail & Related papers  (2021-07-07T22:20:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.