Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching

• URL: http://arxiv.org/abs/2507.07192v3
• Date: Fri, 08 Aug 2025 23:50:27 GMT
• Title: Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching
• Authors: Huibo Xu, Runlong Yu, Likang Wu, Xianquan Wang, Qi Liu,
• Abstract summary: Conditional Guided Flow Matching (CGFM) is a model-agnostic framework that extends flow matching by integrating outputs from an auxiliary predictive model.<n>CGFM incorporates historical data as both conditions and guidance, uses two-sided conditional paths, and employs affine paths to expand the path space.<n> Experiments across datasets and baselines show CGFM consistently outperforms state-of-the-art models, advancing forecasting.
• Score: 9.465542901469815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing generative models for time series forecasting often transform simple priors (typically Gaussian) into complex data distributions. However, their sampling initialization, independent of historical data, hinders the capture of temporal dependencies, limiting predictive accuracy. They also treat residuals merely as optimization targets, ignoring that residuals often exhibit meaningful patterns like systematic biases or nontrivial distributional structures. To address these, we propose Conditional Guided Flow Matching (CGFM), a novel model-agnostic framework that extends flow matching by integrating outputs from an auxiliary predictive model. This enables learning from the probabilistic structure of prediction residuals, leveraging the auxiliary model's prediction distribution as a source to reduce learning difficulty and refine forecasts. CGFM incorporates historical data as both conditions and guidance, uses two-sided conditional paths (with source and target conditioned on the same history), and employs affine paths to expand the path space, avoiding path crossing without complex mechanisms, preserving temporal consistency, and strengthening distribution alignment. Experiments across datasets and baselines show CGFM consistently outperforms state-of-the-art models, advancing forecasting.

Related papers

• Time Series Forecasting via Direct Per-Step Probability Distribution Modeling [30.189067458868326]
  We propose a novel model named interleaved dual-branch Probability Distribution Network (interPDN)<n>It directly constructs discrete probability distributions per step instead of a scalar.<n>Experiments on multiple real-world datasets demonstrate the superior performance of interPDN.
  arXiv  Detail & Related papers  (2025-11-28T15:13:41Z)
• SimDiff: Simpler Yet Better Diffusion Model for Time Series Point Forecasting [8.141505251306622]
  Diffusion models have recently shown promise in time series forecasting.<n>They often fail to achieve state-of-the-art point estimation performance.<n>We propose SimDiff, a single-stage, end-to-end framework for point estimation.
  arXiv  Detail & Related papers  (2025-11-24T16:09:55Z)
• Prequential posteriors [2.831395148295604]
  We introduce prequential posteriors, based upon a predictive-sequential (prequential) loss function.<n>We prove that, under mild conditions, both the prequential loss minimizer and the prequential posterior concentrate around parameters with optimal predictive performance.<n>We validate our method on both a synthetic multi-dimensional time series and a real-world meteorological dataset.
  arXiv  Detail & Related papers  (2025-11-21T19:18:19Z)
• DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment [92.70019102733453]
  Training time-series forecast models requires aligning the conditional distribution of model forecasts with that of the label sequence.<n>We propose DistDF, which achieves alignment by alternatively minimizing a discrepancy between the conditional forecast and label distributions.
  arXiv  Detail & Related papers  (2025-10-28T16:09:59Z)
• Adaptive Conformal Prediction Intervals Over Trajectory Ensembles [50.31074512684758]
  Future trajectories play an important role across domains such as autonomous driving, hurricane forecasting, and epidemic modeling.<n>We propose a unified framework based on conformal prediction that transforms sampled trajectories into calibrated prediction intervals with theoretical coverage guarantees.
  arXiv  Detail & Related papers  (2025-08-18T21:14:07Z)
• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting [52.6508222408558]
  We introduce Elucidated Rolling Diffusion Models (ERDM)<n>ERDM is the first framework to unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM)<n>On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5circ resolution, ERDM consistently outperforms key diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-06-24T21:44:31Z)
• Solving Inverse Problems with FLAIR [59.02385492199431]
  Flow-based latent generative models are able to generate images with remarkable quality, even enabling text-to-image generation.<n>We present FLAIR, a novel training free variational framework that leverages flow-based generative models as a prior for inverse problems.<n>Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.
  arXiv  Detail & Related papers  (2025-06-03T09:29:47Z)
• PO-Flow: Flow-based Generative Models for Sampling Potential Outcomes and Counterfactuals [14.980992014519165]
  PO-Flow is a novel continuous normalizing flow (CNF) framework for causal inference.<n>It provides a unified framework for individualized potential outcome prediction, counterfactual predictions, and uncertainty-aware density learning.<n>It consistently outperforms prior methods across a range of causal inference tasks.
  arXiv  Detail & Related papers  (2025-05-21T22:02:48Z)
• Relational Conformal Prediction for Correlated Time Series [56.59852921638328]
  We address the problem of uncertainty quantification in time series by exploiting correlated sequences.<n>We propose a novel distribution-free approach based on conformal prediction framework and quantile regression.<n>Our approach provides accurate coverage and achieves state-of-the-art uncertainty quantification in relevant benchmarks.
  arXiv  Detail & Related papers  (2025-02-13T16:12:17Z)
• Series-to-Series Diffusion Bridge Model [8.590453584544386]
  We present a comprehensive framework that encompasses most existing diffusion-based methods. We propose a novel diffusion-based time series forecasting model, the Series-to-Series Diffusion Bridge Model ($mathrmS2DBM$) Experimental results demonstrate that $mathrmS2DBM$ delivers superior performance in point-to-point forecasting.
  arXiv  Detail & Related papers  (2024-11-07T07:37:34Z)
• Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting [43.951394031702016]
  We introduce TSFlow, a conditional flow matching (CFM) model for time series combining Gaussian processes, optimal transport paths, and data-dependent prior distributions.<n>We show that both conditionally and unconditionally trained models achieve competitive results across multiple forecasting benchmarks.
  arXiv  Detail & Related papers  (2024-10-03T22:12:50Z)
• Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting [19.383395337330082]
  We propose a generic channel-aware Contrastive Conditional Diffusion model entitled CCDM. The proposed CCDM can exhibit superior forecasting capability compared to current state-of-the-art diffusion forecasters.
  arXiv  Detail & Related papers  (2024-10-03T03:13:15Z)
• Weather Prediction with Diffusion Guided by Realistic Forecast Processes [49.07556359513563]
  We introduce a novel method that applies diffusion models (DM) for weather forecasting. Our method can achieve both direct and iterative forecasting with the same modeling framework. The flexibility and controllability of our model empowers a more trustworthy DL system for the general weather community.
  arXiv  Detail & Related papers  (2024-02-06T21:28:42Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• Counterfactual Explanations for Time Series Forecasting [14.03870816983583]
  We formulate the novel problem of counterfactual generation for time series forecasting, and propose an algorithm, called ForecastCF. ForecastCF solves the problem by applying gradient-based perturbations to the original time series. Our results show that ForecastCF outperforms the baseline in terms of counterfactual validity and data manifold closeness.
  arXiv  Detail & Related papers  (2023-10-12T08:51:59Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)
• Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report [70.7321040534471]
  Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. There is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting.
  arXiv  Detail & Related papers  (2021-09-01T09:52:31Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)

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.