DynG-Diff: A State-Aware Dynamic Guidance Diffusion Framework for Probabilistic Time Series Forecasting
cs.LG
Submitted: 2026-09-02
Updated: 2026-09-02
Code: https://github.com/TT-20011031/DynG-Diff
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation
- Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting
- RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting
- Classifier-Free Diffusion Guidance
- C-RNN-GAN: Continuous recurrent neural networks with adversarial training
- Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
- PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series
- TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation
- $K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
- GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
- Efficiently Modeling Long Sequences with Structured State Spaces
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