{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T14:03:44Z","timestamp":1779631424273,"version":"3.53.1"},"reference-count":33,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:00:00Z","timestamp":1774656000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data"],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:p>\n                    This study proposes an advanced framework for forecasting national tourism revenues by systematically comparing machine learning (ML), deep learning (DL), and hybrid architectures on a country\u2013year panel. Baseline models using only trade and economic indicators have limited explanatory power, whereas adding fiscal, political, and environmental variables substantially improves accuracy. Among ML methods, LightGBM performs best; among DL models, the Transformer excels by capturing nonlinear interactions and temporal dependencies. Building on these results, we introduce a hybrid residual boosting model that integrates the Transformer\u2019s predictive strength with LightGBM\u2019s structural interpretability. The hybrid model outperforms single models across mean absolute error, root mean square error, mean absolute percentage error, and\n                    <jats:italic toggle=\"yes\">R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    , simultaneously minimizing errors and maximizing explanatory power. Methodologically and theoretically, the framework advances tourism economics while offering policymakers actionable guidance on fiscal planning, political stability, and environmental sustainability. Importantly, the empirical results are correlational and reflect predictive associations; they should not be interpreted as causal effects of policy interventions. Methodological novelty lies in a regression-oriented, two-stage residual-boosting design that (1) learns a Transformer as the primary forecaster on the country\u2013year panel, (2) fits LightGBM to the Transformer residuals to correct systematic errors under distributional heterogeneity, and (3) yields a decomposed forecast (base + residual correction) that facilitates transparent error attribution beyond prior DL\u2013DL stacking hybrids. Importantly, the reported relationships are associational and derived from predictive modeling; they should not be interpreted as causal effects of policy levers.\n                  <\/jats:p>","DOI":"10.1177\/2167647x261431683","type":"journal-article","created":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T16:05:20Z","timestamp":1774713920000},"page":"219-243","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Based Forecasting of National Tourism Revenues: Integrating Economic, Fiscal, Political, and Environmental Determinants Through Regression-Oriented Hybrid Models"],"prefix":"10.1177","volume":"14","author":[{"given":"Yueyan","family":"Liu","sequence":"first","affiliation":[{"name":"Qingdao Huanghai University Collage of Design and Fine Arts, Qingdao, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7259-4840","authenticated-orcid":false,"given":"Hyung Jong","family":"Na","sequence":"additional","affiliation":[{"name":"Department of Accounting and Taxation, Semyung University, Jecheon-si, Republic of Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Xue","sequence":"additional","affiliation":[{"name":"Graduate School of Business Administration, Semyung University, Jecheon-si, Republic of Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,3,28]]},"reference":[{"key":"e_1_3_3_2_1","volume-title":"International tourism highlights","author":"World Tourism Organization","year":"2020","unstructured":"1.World Tourism Organization. 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