mlsanityData to 14 Oct 2026

CHIME: Conditional Hallucination and Integrated Multi-scale Enhancement for Time Series Diffusion Model

arXiv 2506.03502 ↗First posted 4 Jun 2025cs.CV cs.SY eess.SYcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

The denoising diffusion probabilistic model has become a mainstream generative model, achieving significant success in various computer vision tasks. Recently, there has been initial exploration of applying diffusion models to time series tasks. However, existing studies still face challenges in multi-scale feature alignment and generative capabilities across different entities and long-time scales. In this paper, we propose CHIME, a conditional hallucination and integrated multi-scale enhancement framework for time series diffusion models. By employing multi-scale decomposition and integration, CHIME captures the decomposed features of time series, achieving in-domain distribution alignment between generated and original samples. In addition, we introduce a feature hallucination module in the conditional denoising process, enabling the temporal features transfer across long-time scales. Experimental results on publicly available real-world datasets demonstrate that CHIME achieves state-of-the-art performance and exhibits excellent generative generalization capabilities in few-shot scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 27 of its 38 references that are in the map. First posted on arXiv 4 Jun 2025.

Builds on

27 of 38 references.

Closest ancestors · 1

Same direction, newest first

  1. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  5. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  6. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  7. 2024Chronos: Learning the Language of Time Seriescited by 759
  8. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  9. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 632
  10. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 582
  11. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 500
  12. 2020Time Series Forecasting With Deep Learning: A Surveycited by 459
14 more
  1. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 337
  2. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 333
  3. 2023ReMoDiffuse: Retrieval-Augmented Motion Diffusion Modelcited by 287
  4. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 198
  5. 2024Diffusion-TS: Interpretable Diffusion for General Time Series Generationcited by 184
  6. 2022PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecastingcited by 172
  7. 2021TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generationcited by 142
  8. 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
  9. 2023Non-autoregressive Conditional Diffusion Models for Time Series Predictioncited by 78
  10. 2021PSA-GAN: Progressive Self Attention GANs for Synthetic Time Seriescited by 58
  11. 2024Retrieval-Augmented Diffusion Models for Time Series Forecastingcited by 39
  12. 2024MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Processcited by 32
  13. 2024Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Predictioncited by 31
  14. 2024Generative Pre-Trained Diffusion Paradigm for Zero-Shot Time Series Forecastingcited by 4

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  1. 2024Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series
  2. 2023ChiroDiff: Modelling chirographic data with Diffusion Models
  3. 2023Diffusion Models for Time Series Applications: A Survey
  4. 2023FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models
  5. 2026TMPDiff: Temporal Mixed-Precision for Diffusion Models
  6. 2023Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations
  7. 2023ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories
  8. 2026Unlocking Diffusion Hierarchies: Adaptive Timestep Selection for Zero-Shot Segmentation