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We thus consider the variational Bayesian STM, which uses variational Bayesian inference to make a reliable judgment of the trend change points without relying on artificial prior information, for our prediction method. With the inferences being driven by the data, our model passes the quantitative uncertainties to the forecast stage of the time series, which improves the robustness and reliability of the model. After conducting several experiments by using a self-collected dataset, we show that compared with a traditional STM, the proposed model has significantly shorter computing times for approximate forecast precision. Moreover, our model improves the forecast efficiency for fuel sales and the synergy of the distributed forecast platform based on an architecture of network.<\/jats:p>","DOI":"10.3233\/jhs-210651","type":"journal-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T13:27:40Z","timestamp":1616160460000},"page":"45-66","source":"Crossref","is-referenced-by-count":5,"title":["A fuel sales forecast method based on variational Bayesian structural time series"],"prefix":"10.1177","volume":"27","author":[{"given":"Huiqiang","family":"Lian","sequence":"first","affiliation":[{"name":"School of Engineering Science, University of Chinese Academy of Sciences, No. 19(A) Yuquan Road, Shijingshan District, Beijing, P.R. China"},{"name":"PetroChina Hebei Branch Company, No. 9 Shiqing Road, Xinhua District, Shijizhuang, Hebei, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Liu","sequence":"additional","affiliation":[{"name":"DingLi Corporation Ltd, No. 8 Keji 5th Road, Tangjiawan Town, Gaoxin District, Zhuhai, Guangdong, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengyuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Engineering Science, University of Chinese Academy of Sciences, No. 19(A) Yuquan Road, Shijingshan District, Beijing, P.R. 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