Towards Stable and Structured Time Series Generation with Perturbation-Aware Flow Matching

• URL: http://arxiv.org/abs/2511.14488v1
• Date: Tue, 18 Nov 2025 13:30:56 GMT
• Title: Towards Stable and Structured Time Series Generation with Perturbation-Aware Flow Matching
• Authors: Jintao Zhang, Mingyue Cheng, Zirui Liu, Xianquan Wang, Yitong Zhou, Qi Liu,
• Abstract summary: We introduce textbfPAFM, a framework that models perturbed trajectories to ensure stable and structurally consistent time series generation.<n>The framework incorporates perturbation-guided training to simulate localized disturbances and leverages a dual-path velocity field to capture trajectory deviations under perturbation.<n>In experiments on both unconditional and conditional generation tasks, PAFM consistently outperforms strong baselines.
• Score: 16.17115009663765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series generation is critical for a wide range of applications, which greatly supports downstream analytical and decision-making tasks. However, the inherent temporal heterogeneous induced by localized perturbations present significant challenges for generating structurally consistent time series. While flow matching provides a promising paradigm by modeling temporal dynamics through trajectory-level supervision, it fails to adequately capture abrupt transitions in perturbed time series, as the use of globally shared parameters constrains the velocity field to a unified representation. To address these limitations, we introduce \textbf{PAFM}, a \textbf{P}erturbation-\textbf{A}ware \textbf{F}low \textbf{M}atching framework that models perturbed trajectories to ensure stable and structurally consistent time series generation. The framework incorporates perturbation-guided training to simulate localized disturbances and leverages a dual-path velocity field to capture trajectory deviations under perturbation, enabling refined modeling of perturbed behavior to enhance the structural coherence. In order to further improve sensitivity to trajectory perturbations while enhancing expressiveness, a mixture-of-experts decoder with flow routing dynamically allocates modeling capacity in response to different trajectory dynamics. Extensive experiments on both unconditional and conditional generation tasks demonstrate that PAFM consistently outperforms strong baselines. Code is available at https://anonymous.4open.science/r/PAFM-03B2.

Related papers

• FoSS: Modeling Long Range Dependencies and Multimodal Uncertainty in Trajectory Prediction via Fourier State Space Integration [21.39395366378851]
  We present FoSS, a dual-branch framework that unifies frequency-domain reasoning with linear-time sequence modeling.<n>Experiments on Argoverse 1 and Argoverse 2 benchmarks demonstrate that FoSS achieves state-of-the-art accuracy while reducing computation by 22.5% and parameters by over 40%.
  arXiv  Detail & Related papers  (2026-03-01T21:38:59Z)
• Koopman Autoencoders with Continuous-Time Latent Dynamics for Fluid Dynamics Forecasting [17.98687936773676]
  We introduce a continuous-time Koopman framework that models latent evolution through numerical integration schemes.<n>By allowing variable timesteps at inference, the method demonstrates robustness to temporal resolution and generalizes beyond training regimes.<n>We evaluate the approach on classical CFD benchmarks and report accuracy, stability, and extrapolation properties.
  arXiv  Detail & Related papers  (2026-02-02T21:33:07Z)
• Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics [51.85385061275941]
  Molecular dynamics (MD) simulations remain the gold standard for studying protein dynamics.<n>Recent generative models have shown promise in accelerating simulations, yet they struggle with long-horizon generation.<n>We present STAR-MD, a scalable diffusion model that generates physically plausible protein trajectories over micro-scale timescales.
  arXiv  Detail & Related papers  (2026-02-02T14:13:28Z)
• ACFormer: Mitigating Non-linearity with Auto Convolutional Encoder for Time Series Forecasting [6.27761817493579]
  Time series forecasting (TSF) faces challenges in modeling complex intra-channel temporal dependencies and inter-channel correlations.<n>We propose ACFormer, an architecture designed to reconcile the efficiency of linear projections with the non-linear feature-extraction power of convolutions.
  arXiv  Detail & Related papers  (2026-01-28T13:47:54Z)
• Error-Propagation-Free Learned Video Compression With Dual-Domain Progressive Temporal Alignment [92.57576987521107]
  We propose a novel unifiedtransform framework with dual-domain progressive temporal alignment and quality-conditioned mixture-of-expert (QCMoE)<n>QCMoE allows continuous and consistent rate control with appealing R-D performance.<n> Experimental results show that the proposed method achieves competitive R-D performance compared with the state-of-the-arts.
  arXiv  Detail & Related papers  (2025-12-11T09:14:51Z)
• Structured Noise Modeling for Enhanced Time-Series Forecasting [0.0]
  This work introduces a forecast-blur-denoise framework that improves temporal fidelity through structured noise modeling.<n> Experiments across electricity, traffic, and solar datasets show consistent gains in multi-horizon accuracy and stability.<n>This framework contributes to more trustworthy AI systems used in forecasting-driven decision support across energy, infrastructure, and other time-critical domains.
  arXiv  Detail & Related papers  (2025-11-24T19:44:46Z)
• TimeFlow: Towards Stochastic-Aware and Efficient Time Series Generation via Flow Matching Modeling [2.74279932215302]
  Time series data has emerged as a critical research topic due to its broad utility in supporting downstream time series mining tasks.<n>We propose TimeFlow, a novel flow matching framework that integrates a encoder-only architecture.<n>Our model consistently outperforms strong baselines in generation quality, diversity, and efficiency.
  arXiv  Detail & Related papers  (2025-11-11T08:28:26Z)
• RainDiff: End-to-end Precipitation Nowcasting Via Token-wise Attention Diffusion [64.49056527678606]
  We propose a Token-wise Attention integrated into not only the U-Net diffusion model but also the radar-temporal encoder.<n>Unlike prior approaches, our method integrates attention into the architecture without incurring the high resource cost typical of pixel-space diffusion.<n>Our experiments and evaluations demonstrate that the proposed method significantly outperforms state-of-the-art approaches, robustness local fidelity, generalization, and superior in complex precipitation forecasting scenarios.
  arXiv  Detail & Related papers  (2025-10-16T17:59:13Z)
• 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)
• FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems [51.15230303652732]
  FLEX (F Low EXpert) is a backbone architecture for generative modeling of-temporal physical systems.<n>It reduces the variance of the velocity field in the diffusion model, which helps stabilize training.<n>It achieves accurate predictions for super-resolution and forecasting tasks using as few features as two reverse diffusion steps.
  arXiv  Detail & Related papers  (2025-05-23T00:07:59Z)
• Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models [71.63194926457119]
  We introduce Dynamical Diffusion (DyDiff), a theoretically sound framework that incorporates temporally aware forward and reverse processes.<n>Experiments across scientifictemporal forecasting, video prediction, and time series forecasting demonstrate that Dynamical Diffusion consistently improves performance in temporal predictive tasks.
  arXiv  Detail & Related papers  (2025-03-02T16:10:32Z)
• Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series [14.400596021890863]
  Many real-world datasets, such as healthcare, climate, and economics, are often collected as irregular time series.<n>We propose the Amortized Control of continuous State Space Model (ACSSM) for continuous dynamical modeling of time series.
  arXiv  Detail & Related papers  (2024-10-08T01:27:46Z)
• 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)
• Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation [13.278744447861289]
  We introduce a novel generative framework grounded in probabilistic diffusion models for turbulence generation. A notable feature of our approach is the proposed method for long-span flow sequence generation, which is based on autoregressive-based conditional sampling. We showcase the versatile turbulence generation capability of our framework through a suite of numerical experiments.
  arXiv  Detail & Related papers  (2023-11-14T04:08:14Z)
• Predicting the temporal dynamics of turbulent channels through deep learning [0.0]
  We aim to assess the capability of neural networks to reproduce the temporal evolution of a minimal turbulent channel flow. Long-short-term-memory (LSTM) networks and a Koopman-based framework (KNF) are trained to predict the temporal dynamics of the minimal-channel-flow modes.
  arXiv  Detail & Related papers  (2022-03-02T09:31:03Z)

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.