{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:14:35Z","timestamp":1760710475034,"version":"3.41.2"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"vor","delay-in-days":32,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, <jats:italic>N<\/jats:italic>) model is the most popular and important grey model, in which the first \u201c1\u201d stands for the \u201cfirst order\u201d and the second \u201c<jats:italic>N<\/jats:italic>\u201d represents the \u201cmultivariate.\u201d The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, <jats:italic>N<\/jats:italic>) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models.<\/jats:p>","DOI":"10.1155\/2021\/6663773","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:10:14Z","timestamp":1612311014000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application"],"prefix":"10.1155","volume":"2021","author":[{"given":"Tongfei","family":"Lao","sequence":"first","affiliation":[]},{"given":"Xiaoting","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1951-8382","authenticated-orcid":false,"given":"Jianian","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1080\/15567249.2017.1423413"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2018.2846646"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.05.031"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-6911(82)80025-X"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.123903"},{"key":"e_1_2_8_6_2","article-title":"MFO-based conformable fractional nonhomogeneous grey Bernoulli model for natural gas production and consumption forecasting","volume":"278","author":"Zheng C. 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