{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T10:04:25Z","timestamp":1761213865244,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:00:00Z","timestamp":1761177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12101090"],"award-info":[{"award-number":["12101090"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018542","name":"Sichuan Natural Science Foundation","doi-asserted-by":"publisher","award":["2023NSFSC0071","2023NSFSC1362"],"award-info":[{"award-number":["2023NSFSC0071","2023NSFSC1362"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2023ZYD0001","2021ZYD0009"],"award-info":[{"award-number":["2023ZYD0001","2021ZYD0009"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chengdu University of Information Technology Science and Technology Innovation Capability Enhancement Plan Innovation Team Key Project","award":["KYTD202322","KYTD202226"],"award-info":[{"award-number":["KYTD202322","KYTD202226"]}]},{"name":"General Projects of Local Science Technology Development Funds Guided by the Central Government","award":["2022ZYD0005"],"award-info":[{"award-number":["2022ZYD0005"]}]},{"name":"Talent Introduction Program of Chengdu University of Information Technology","award":["KYTZ202185"],"award-info":[{"award-number":["KYTZ202185"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This paper addresses the cold-start problem and rare event prediction challenges in Olympic medal forecasting by proposing a predictive framework that integrates multi-granularity transfer learning with extreme value theory. The framework comprises two main components, a Multi-Granularity Transfer Learning Core (MG-TLC) and a Rare Event Analysis Module (RE-AM), which address multi-level prediction for data-scarce countries and first medal prediction tasks. The MG-TLC incorporates two key components: Dynamic Feature Space Reconstruction (DFSR) and the Hierarchical Adaptive Transfer Strategy (HATS). The RE-AM combines a Bayesian hierarchical extreme value model (BHEV) with piecewise survival analysis (PSA). Experiments based on comprehensive, licensed Olympic data from 1896\u20132024, where the framework was trained on data up to 2016, validated on the 2020 Games, and tested by forecasting the 2024 Games, demonstrate that the proposed framework significantly outperforms existing methods, reducing MAE by 25.7% for data-scarce countries and achieving an AUC of 0.833 for first medal prediction, 14.3% higher than baseline methods. This research establishes a foundation for predicting the 2028 Los Angeles Olympics and provides new approaches for cold-start and rare event prediction, with potential applicability to similar challenges in other data-scarce domains such as economics or public health. From a symmetry viewpoint, our framework is designed to preserve task-relevant invariances\u2014permutation invariance in set-based country aggregation and scale robustness to macro-covariate units\u2014via distributional alignment between data-rich and data-scarce domains and Olympic-cycle indexing. We treat departures from these symmetries (e.g., host advantage or event-program changes) as structured asymmetries and capture them with a rare event module that combines extreme value and survival modeling.<\/jats:p>","DOI":"10.3390\/sym17111791","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T09:44:30Z","timestamp":1761212670000},"page":"1791","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Symmetry-Aware Predictive Framework for Olympic Cold-Start Problems and Rare Events Based on Multi-Granularity Transfer Learning and Extreme Value Analysis"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0137-6917","authenticated-orcid":false,"given":"Yanan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Applied Mathematics, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0000-5655","authenticated-orcid":false,"given":"Yi","family":"Fei","sequence":"additional","affiliation":[{"name":"College of Applied Mathematics, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4560-1286","authenticated-orcid":false,"given":"Qiuyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Applied Mathematics, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1162\/003465304774201824","article-title":"Who wins the Olympic Games: Economic resources and medal totals","volume":"86","author":"Bernard","year":"2004","journal-title":"Rev. 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