TimePre unifies MLP speed and MCL distributional power via Stabilized Instance Normalization to deliver SOTA probabilistic accuracy, orders-of-magnitude faster inference, and improved stability over prior MCL methods.
Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting.CoRR, abs/2101.12072
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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.
A synthesis of diffusion-based simulation-based inference methods that address model misspecification, irregular observations, and missing data in scientific applications.
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TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
TimePre unifies MLP speed and MCL distributional power via Stabilized Instance Normalization to deliver SOTA probabilistic accuracy, orders-of-magnitude faster inference, and improved stability over prior MCL methods.
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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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A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios
A synthesis of diffusion-based simulation-based inference methods that address model misspecification, irregular observations, and missing data in scientific applications.