Effective Series Decomposition and Components Learning for Time Series Generation

• URL: http://arxiv.org/abs/2511.00747v1
• Date: Sun, 02 Nov 2025 00:15:12 GMT
• Title: Effective Series Decomposition and Components Learning for Time Series Generation
• Authors: Zixuan Ma, Chenfeng Huang,
• Abstract summary: We introduce Seasonal-Trend Diffusion (STDiffusion), a novel framework for time series generation.<n>STDiffusion integrates probabilistic models with advanced learnable series decomposition techniques.<n>Our studies demonstrate that STDiffusion achieves reliable results and highlighted its robustness and versatility.
• Score: 2.4675755217083317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series generation focuses on modeling the underlying data distribution and resampling to produce authentic time series data. Key components, such as trend and seasonality, drive temporal fluctuations, yet many existing approaches fail to employ interpretative decomposition methods, limiting their ability to synthesize meaningful trend and seasonal patterns. To address this gap, we introduce Seasonal-Trend Diffusion (STDiffusion), a novel framework for multivariate time series generation that integrates diffusion probabilistic models with advanced learnable series decomposition techniques, enhancing the interpretability of the generation process. Our approach separates the trend and seasonal learning into distinct blocks: a Multi-Layer Perceptron (MLP) structure captures the trend, while adaptive wavelet distillation facilitates effective multi-resolution learning of seasonal components. This decomposition improves the interpretability of the model on multiple scales. In addition, we designed a comprehensive correction mechanism aimed at ensuring that the generated components exhibit a high degree of internal consistency and preserve meaningful interrelationships with one another. Our empirical studies on eight real-world datasets demonstrate that STDiffusion achieves state-of-the-art performance in time series generation tasks. Furthermore, we extend the model's application to multi-window long-sequence time series generation, which delivered reliable results and highlighted its robustness and versatility.

Related papers

• UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting [90.47915032778366]
  We propose UniDiff, a unified diffusion framework for multimodal time series forecasting.<n>At its core lies a unified and parallel fusion module, where a single cross-attention mechanism integrates structural information from timestamps and semantic context from texts.<n>Experiments on real-world benchmark datasets across eight domains demonstrate that the proposed UniDiff model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-08T05:36:14Z)
• A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting [81.73338008264115]
  Current approaches for time series forecasting, whether in the time or frequency domain, predominantly use deep learning models based on linear layers or transformers.<n>We propose FIRE, a unified frequency domain decomposition framework that provides a mathematical abstraction for diverse types of time series.<n>Fire consistently outperforms state-of-the-art models on long-term forecasting benchmarks.
  arXiv  Detail & Related papers  (2025-10-11T09:59:25Z)
• TIMED: Adversarial and Autoregressive Refinement of Diffusion-Based Time Series Generation [0.31498833540989407]
  TIMED is a unified generative framework that captures global structure via a forward-reverse diffusion process.<n>To further align the real and synthetic distributions in feature space, TIMED incorporates a Maximum Mean Discrepancy (MMD) loss.<n>We show that TIMED generates more realistic and temporally coherent sequences than state-of-the-art generative models.
  arXiv  Detail & Related papers  (2025-09-23T23:05:40Z)
• CHIME: Conditional Hallucination and Integrated Multi-scale Enhancement for Time Series Diffusion Model [6.893910847034251]
  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.
  arXiv  Detail & Related papers  (2025-06-04T02:34:09Z)
• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies.<n>We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis.<n> Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• Ister: Inverted Seasonal-Trend Decomposition Transformer for Explainable Multivariate Time Series Forecasting [10.32586981170693]
  Inverted Seasonal-Trend Decomposition Transformer (Ister)<n>We introduce a novel Dot-attention mechanism that improves interpretability, computational efficiency, and predictive accuracy.<n>Ister enables intuitive visualization of component contributions, shedding lights on model's decision process and enhancing transparency in prediction results.
  arXiv  Detail & Related papers  (2024-12-25T06:37:19Z)
• A Decomposition Modeling Framework for Seasonal Time-Series Forecasting [0.0]
  Seasonal time series exhibit intricate long-term dependencies.<n>This paper introduces the Multi-scale Seasonal Decomposition Model (MSSD) for seasonal time-series forecasting.
  arXiv  Detail & Related papers  (2024-12-12T01:37:25Z)
• Conditional Denoising Meets Polynomial Modeling: A Flexible Decoupled Framework for Time Series Forecasting [5.770377200028654]
  We propose a Conditional Denoising Polynomial Modeling (CDPM) framework to model complicated temporal patterns.<n>Instead of modeling the coupled time series, CDPM decomposes it into trend and seasonal components for modeling them separately.<n>For the smooth trend component, a module is proposed to enhance linear models by incorporating historical dependencies.
  arXiv  Detail & Related papers  (2024-10-17T06:20:43Z)
• 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)
• Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective [63.60312929416228]
  textbftextitAttraos incorporates chaos theory into long-term time series forecasting. We show that Attraos outperforms various LTSF methods on mainstream datasets and chaotic datasets with only one-twelfth of the parameters compared to PatchTST.
  arXiv  Detail & Related papers  (2024-02-18T05:35:01Z)
• 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)
• Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection [2.2559617939136505]
  This paper explores some of the challenges in Robust Anomaly Detection (AD) on time series data. We propose a new approach that makes inroads towards increased robustness on seasonal and contaminated data. We conduct extensive experiments in which not only do this model displays more robust behavior on complex seasonality patterns, but also shows increased resistance to training data contamination.
  arXiv  Detail & Related papers  (2020-07-25T01:32:00Z)

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.