{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T02:08:21Z","timestamp":1783649301927,"version":"3.55.0"},"reference-count":50,"publisher":"Emerald","issue":"1","funder":[{"name":"the National Social Science Found of China","award":["24BTJ063"],"award-info":[{"award-number":["24BTJ063"]}]},{"name":"the Graduate Student Scientific Research Project of Chongqing Technology and Business University","award":["yjscxx2025 - 269 - 108"],"award-info":[{"award-number":["yjscxx2025 - 269 - 108"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,21]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This paper aims to propose an improved seasonal fractional order discrete grey multivariate model to more accurately predict data series with seasonal and periodic characteristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>A seasonal differentiated fractional order grey multivariate model considering interaction effect is constructed by introducing interactive and seasonal driving terms, as well as differentiated accumulative orders. The new model considers the interaction effect between the transverse and longitudinal evolution. Moreover, differentiated accumulation orders are set for heterogeneous variables. To further enhance the model, an average weakening buffer operator is further introduced. Secondly, the optimal algorithm is screened through the Monte Carlo method. The robustness and noise sensitivity are analyzed through comparative analysis and statistical tests. Finally, the new model is applied to the quarterly sales of new energy vehicles in China.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>Validation results demonstrate significant advantages of the novel model for seasonal and cyclical data, exhibiting minimal error, highest stability, and lowest noise sensitivity. In addition, the results of predicting the future quarterly sales of new energy vehicles demonstrate a year-by-year increase in sales and a gradual decrease in periodic volatility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>The new model integrates interaction, seasonality, and differentiated accumulation orders, demonstrating strong fitting and forecasting capabilities for seasonal and periodic data. Additionally, it can be effectively used to forecast new energy vehicle quarterly sales in China, aiming to provide more precise data support for automakers to optimize production capacity allocation and enhance supply chain collaboration.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/gs-04-2025-0035","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T04:27:43Z","timestamp":1768883263000},"page":"1-24","source":"Crossref","is-referenced-by-count":1,"title":["A novel seasonal grey multivariate model and its application in China\u2019s new energy vehicle quarterly sales"],"prefix":"10.1108","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2948-9124","authenticated-orcid":true,"given":"Shuliang","family":"Li","sequence":"first","affiliation":[{"name":"School of Management Science and Engineering, Chongqing Technology and Business University , ,","place":["Chongqing, 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