EMFusion applies conditional diffusion models with cross-attention and imputation sampling to deliver uncertainty-aware probabilistic forecasts for frequency-selective EMF data, outperforming baselines by 23.85% in CRPS.
Diffusion-based time series imputa- tion and forecasting with structured state space models
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SDFlow learns a global transport map via similarity-driven flow matching in VQ latent space, using low-rank manifold decomposition and a categorical posterior to handle discreteness, yielding SOTA long-horizon performance and inference speedups.
CondI applies conditional diffusion models in a two-phase federated pipeline to impute within-modality missing data, then trains extractors on the completed inputs for downstream tasks on clinical datasets.
DRIO adds worst-case Wasserstein regularization to time series imputation, yielding a tractable adversarial surrogate and alternating algorithm that improves robustness under missingness.
NsDiff combines a denoising diffusion conditional generative model with a pre-trained mean/variance estimator and an uncertainty-aware noise schedule based on the Location-Scale Noise Model to capture time-varying uncertainty in probabilistic forecasting.
MR-CDM uses hierarchical multi-resolution decomposition and multi-scale conditional diffusion to generate forecasts that reduce MAE and RMSE by 6-10% versus baselines like CSDI and Informer on four datasets.
citing papers explorer
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EMFusion: An Uncertainty-Aware Conditional Diffusion Framework for Frequency-Selective EMF Forecasting in Wireless Networks
EMFusion applies conditional diffusion models with cross-attention and imputation sampling to deliver uncertainty-aware probabilistic forecasts for frequency-selective EMF data, outperforming baselines by 23.85% in CRPS.
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SDFlow: Similarity-Driven Flow Matching for Time Series Generation
SDFlow learns a global transport map via similarity-driven flow matching in VQ latent space, using low-rank manifold decomposition and a categorical posterior to handle discreteness, yielding SOTA long-horizon performance and inference speedups.
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Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning
CondI applies conditional diffusion models in a two-phase federated pipeline to impute within-modality missing data, then trains extractors on the completed inputs for downstream tasks on clinical datasets.
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Multivariate Time Series Data Imputation via Distributionally Robust Regularization
DRIO adds worst-case Wasserstein regularization to time series imputation, yielding a tractable adversarial surrogate and alternating algorithm that improves robustness under missingness.
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Non-stationary Diffusion For Probabilistic Time Series Forecasting
NsDiff combines a denoising diffusion conditional generative model with a pre-trained mean/variance estimator and an uncertainty-aware noise schedule based on the Location-Scale Noise Model to capture time-varying uncertainty in probabilistic forecasting.
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MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations
MR-CDM uses hierarchical multi-resolution decomposition and multi-scale conditional diffusion to generate forecasts that reduce MAE and RMSE by 6-10% versus baselines like CSDI and Informer on four datasets.