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Then uncertain autoregressive moving average (UARMA) model can be used to fit the residuals sequence. After that, based on the uncertain statistics methods, the corresponding point prediction and interval prediction can be performed, which can provide more favorable support for the decision makers. Finally, the real passenger flow data of London underground are analyzed and predicted, and the results illustrate that the UARMA model is more reasonable and effective than traditional ARMA model.<\/jats:p>","DOI":"10.1142\/s1752890925500217","type":"journal-article","created":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T05:29:33Z","timestamp":1758950973000},"source":"Crossref","is-referenced-by-count":0,"title":["Uncertain Autoregressive Moving Average Analysis with Application to Metro Passenger Flow Forecast"],"prefix":"10.1142","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5830-9516","authenticated-orcid":false,"given":"Shize","family":"Ning","sequence":"first","affiliation":[{"name":"School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, P. R. 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