{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:07Z","timestamp":1761176227794,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Accurate time series forecasting is crucial across various domains, yet modeling complex, non-stationary time series remains challenging. Recent advancements in diffusion models have inspired their application to time series. However, existing approaches struggle to capture historical patterns and temporal dependencies essential to realize data distributions. Additionally, the reliance on RNN or Transformer architectures intensifies computational costs due to the iterative nature of the diffusion process. To address these shortcomings, we propose a novel diffusion-based model employing a conditional non-autoregressive diffusion process. Specifically, we propose a Historical-Free Guidance (HFG) approach to regulate the denoising process. This approach leverages a state-space-based encoder capable of efficiently extracting latent patterns from historical observations with linear computational complexity. A complementary Temporal encoder is employed to capture temporal dynamics. These representations are then integrated as conditional information and transmitted to a guidance gate that discards the conditional information with a predefined probability. This strategy enables the model to operate seamlessly as both a conditional and unconditional model without added complexity. Comprehensive evaluations conducted on six real-world datasets demonstrate the superior performance of our proposed model compared to established baselines. Notably, the model outperforms the second-best model, achieving average reductions of 11.44% in Mean Squared Error (MSE) and 22.28% in Continuous Ranked Probability Score (CRPS).<\/jats:p>","DOI":"10.3233\/faia251163","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:18Z","timestamp":1761126798000},"source":"Crossref","is-referenced-by-count":0,"title":["Guide the Diffusion: A Guidance Diffusion Approach to Time Series Forecasting"],"prefix":"10.3233","author":[{"given":"Yihang","family":"He","sequence":"first","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khaled","family":"Alkilane","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Der-Horng","family":"Lee","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251163","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:18Z","timestamp":1761126798000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251163","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}