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

• URL: http://arxiv.org/abs/2506.03502v2
• Date: Fri, 04 Jul 2025 07:41:33 GMT
• Title: CHIME: Conditional Hallucination and Integrated Multi-scale Enhancement for Time Series Diffusion Model
• Authors: Yuxuan Chen, Haipeng Xie,
• Abstract summary: CHIME is a conditional hallucination and integrated multi-scale enhancement framework for time series diffusion models.<n>It captures decomposed features of time series, achieving in-domain distribution alignment between generated and original samples.<n>In addition, we introduce a feature hallucination module in the conditional denoising process, enabling the temporal features transfer across long-time scales.
• Score: 6.893910847034251
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach [7.631288333466648]
  We conduct the first large-scale study evaluating leading generative models in data-scarce settings.<n>We propose a unified diffusion-based generative framework that can synthesize high-fidelity time series using just a few examples.
  arXiv  Detail & Related papers  (2025-05-26T18:39:04Z)
• Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting [64.45587649141842]
  Time-series forecasting plays a critical role in many real-world applications.<n>No single model consistently outperforms others across different test samples, but instead (ii) each model excels in specific cases.<n>We introduce TimeFuse, a framework for collective time-series forecasting with sample-level adaptive fusion of heterogeneous models.
  arXiv  Detail & Related papers  (2025-05-24T00:45:07Z)
• Vision-Enhanced Time Series Forecasting via Latent Diffusion Models [12.54316645614762]
  LDM4TS is a novel framework that leverages the powerful image reconstruction capabilities of latent diffusion models for vision-enhanced time series forecasting.<n>We are the first to use complementary transformation techniques to convert time series into multi-view visual representations.
  arXiv  Detail & Related papers  (2025-02-16T14:15:06Z)
• General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data [61.163542597764796]
  We show that time series with different time granularities (or corresponding frequency resolutions) exhibit distinct joint distributions in the frequency domain.<n>A novel Fourier knowledge attention mechanism is proposed to enable learning time-aware representations from both the temporal and frequency domains.<n>An autoregressive blank infilling pre-training framework is incorporated to time series analysis for the first time, leading to a generative tasks agnostic pre-training strategy.
  arXiv  Detail & Related papers  (2025-02-05T15:20:04Z)
• Retrieval-Augmented Diffusion Models for Time Series Forecasting [19.251274915003265]
  We propose a Retrieval- Augmented Time series Diffusion model (RATD) RATD consists of two parts: an embedding-based retrieval process and a reference-guided diffusion model. Our approach allows leveraging meaningful samples within the database to aid in sampling, thus maximizing the utilization of datasets.
  arXiv  Detail & Related papers  (2024-10-24T13:14:39Z)
• Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts [103.725112190618]
  This paper introduces Moirai-MoE, using a single input/output projection layer while delegating the modeling of diverse time series patterns to the sparse mixture of experts. Extensive experiments on 39 datasets demonstrate the superiority of Moirai-MoE over existing foundation models in both in-distribution and zero-shot scenarios.
  arXiv  Detail & Related papers  (2024-10-14T13:01:11Z)
• Stochastic Diffusion: A Diffusion Based Model for Stochastic Time Series Forecasting [8.232475807691255]
  We propose a novel Diffusion (StochDiff) model which learns data-driven prior knowledge at each time step.<n>The learnt prior knowledge helps the model to capture complex temporal dynamics and the inherent uncertainty of the data.
  arXiv  Detail & Related papers  (2024-06-05T00:13:38Z)
• MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process [26.661721555671626]
  We introduce a novel Multi-Granularity Time Series (MG-TSD) model, which achieves state-of-the-art predictive performance. Our approach does not rely on additional external data, making it versatile and applicable across various domains.
  arXiv  Detail & Related papers  (2024-03-09T01:15:03Z)
• PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations [49.80959046861793]
  We present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers. Our experimentation across seven diversetemporal real-world LMTF datasets reveals that PDETime adapts effectively to the intrinsic nature of the data.
  arXiv  Detail & Related papers  (2024-02-25T17:39:44Z)
• Timer: Generative Pre-trained Transformers Are Large Time Series Models [83.03091523806668]
  This paper aims at the early development of large time series models (LTSM) During pre-training, we curate large-scale datasets with up to 1 billion time points. To meet diverse application needs, we convert forecasting, imputation, and anomaly detection of time series into a unified generative task.
  arXiv  Detail & Related papers  (2024-02-04T06:55:55Z)
• TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting [24.834846119163885]
  We propose a novel framework, TEMPO, that can effectively learn time series representations. TEMPO expands the capability for dynamically modeling real-world temporal phenomena from data within diverse domains.
  arXiv  Detail & Related papers  (2023-10-08T00:02:25Z)
• Collaborative Diffusion for Multi-Modal Face Generation and Editing [34.16906110777047]
  We present Collaborative Diffusion, where pre-trained uni-modal diffusion models collaborate to achieve multi-modal face generation and editing without re-training. Specifically, we propose dynamic diffuser, a meta-network that adaptively hallucinates multi-modal denoising steps by predicting the spatial-temporal influence functions for each pre-trained uni-modal model.
  arXiv  Detail & Related papers  (2023-04-20T17:59:02Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.