{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:10Z","timestamp":1761176230415,"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>The periodicity of time series has significantly advanced long-term forecasting and has attracted extensive research efforts. However, existing methods still suffer from neglecting critical low-energy periodic components and high sensitivity to outliers. To address these issues, we propose the PeriOdic Spectra Transition via MAmba Network (POSTMAN). This architecture introduces the periodic spectrum deviation forecasting (PSDF) technique, which extracts the shared spectrum to represent the common periodic features and generates deviation spectra to represent the specific periodic features. The shared periodic spectrum retains the critical low-amplitude components, while the deviation spectra preserve the slight differences between periods. To effectively leverage the differences, we develop a spectral convolution-enhanced Frequency Mamba Block (FMB), which learns the transition patterns of periodic deviation spectra and inhibits the impact of outliers during the transition procedure. Experiments on seven mainstream time series datasets demonstrate that POSTMAN outperforms existing state-of-the-art models in accuracy and robustness.<\/jats:p>","DOI":"10.3233\/faia251177","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:44Z","timestamp":1761126824000},"source":"Crossref","is-referenced-by-count":0,"title":["POSTMAN: Periodic Spectra Transition via Mamba Network for Time Series Forecasting"],"prefix":"10.3233","author":[{"given":"Kaixin","family":"Zhao","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Su","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijun","family":"Mo","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology"}],"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\/FAIA251177","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:44Z","timestamp":1761126824000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251177","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]]}}}