TRACE proposes a temporal conditional estimation paradigm for multimodal time series foundation models that infers incomplete target modalities from auxiliary ones, outperforming prior fusion methods on clinical and sentiment benchmarks under missingness.
Diffusion-based time series imputa- tion and forecasting with structured state space models
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SRT decomposes low-resolution time series into trend and seasonal components, aligns them via implicit neural representations, and uses cross-resolution attention within a disentangled rectified flow to generate high-resolution outputs, with a scaled SRT-large variant for zero-shot use.
Proves SLiCEs are universal time-series generators approximating path laws in W_∞ and proposes G-SLiCEs for path-space flow matching with benefits on irregular grids.
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.
PRDIM is a diffusion model using a pattern recognizer to impute MNAR missing data by maximizing joint likelihood of observed values and missing mask via EM.
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.
On an 8-month Italian hospital EMF dataset, a working-hour-conditioned multivariate diffusion forecaster beats the best baseline in CRPS and NRMSE, though the headline percentages do not match the paper's own aggregate table.
citing papers explorer
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TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models
TRACE proposes a temporal conditional estimation paradigm for multimodal time series foundation models that infers incomplete target modalities from auxiliary ones, outperforming prior fusion methods on clinical and sentiment benchmarks under missingness.
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SRT: Super-Resolution for Time Series via Disentangled Rectified Flow
SRT decomposes low-resolution time series into trend and seasonal components, aligns them via implicit neural representations, and uses cross-resolution attention within a disentangled rectified flow to generate high-resolution outputs, with a scaled SRT-large variant for zero-shot use.
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Universal Time Series Generation with Neural Controlled Differential Equations
Proves SLiCEs are universal time-series generators approximating path laws in W_∞ and proposes G-SLiCEs for path-space flow matching with benefits on irregular grids.
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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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Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random
PRDIM is a diffusion model using a pattern recognizer to impute MNAR missing data by maximizing joint likelihood of observed values and missing mask via EM.
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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.
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EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting
On an 8-month Italian hospital EMF dataset, a working-hour-conditioned multivariate diffusion forecaster beats the best baseline in CRPS and NRMSE, though the headline percentages do not match the paper's own aggregate table.