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

• URL: http://arxiv.org/abs/2507.07192v2
• Date: Mon, 14 Jul 2025 03:33:58 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: Flow matching offers faster generation, higher-quality outputs, and greater flexibility.<n> Conditional Guided Flow Matching (CGFM) extends flow matching by incorporating the outputs of an auxiliary model.<n>For time series forecasting tasks, CGFM integrates historical data as conditions and guidance, constructs two-sided conditional probability paths, and uses a general affine path to expand the space of probability paths.
• Score: 9.465542901469815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models, a type of generative model, have shown promise in time series forecasting. But they face limitations like rigid source distributions and limited sampling paths, which hinder their performance. Flow matching offers faster generation, higher-quality outputs, and greater flexibility, while also possessing the ability to utilize valuable information from the prediction errors of prior models, which were previously inaccessible yet critically important. To address these challenges and fully unlock the untapped potential of flow matching, we propose Conditional Guided Flow Matching (CGFM). CGFM extends flow matching by incorporating the outputs of an auxiliary model, enabling a previously unattainable capability in the field: learning from the errors of the auxiliary model. For time series forecasting tasks, it integrates historical data as conditions and guidance, constructs two-sided conditional probability paths, and uses a general affine path to expand the space of probability paths, ultimately leading to improved predictions. Extensive experiments show that CGFM consistently enhances and outperforms state-of-the-art models, highlighting its effectiveness in advancing forecasting methods.

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