Please use this identifier to cite or link to this item: https://elib.vku.udn.vn/handle/123456789/4300
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dc.contributor.authorPhan, Van Thanh-
dc.contributor.authorNguyen, Ngoc Thang-
dc.contributor.authorVu, Van Vien-
dc.date.accessioned2024-12-09T02:20:29Z-
dc.date.available2024-12-09T02:20:29Z-
dc.date.issued2024-11-
dc.identifier.isbn978-3-031-74126-5-
dc.identifier.urihttps://elib.vku.udn.vn/handle/123456789/4300-
dc.identifier.urihttps://doi.org/10.1007/978-3-031-74127-2_37-
dc.descriptionLecture Notes in Networks and Systems (LNNS,volume 882); The 13th Conference on Information Technology and Its Applications (CITA 2024) ; pp: 461-469.vi_VN
dc.description.abstractIn this paper, we propose a new systematic approach for improving the prediction performance of GMr (1, 1) by optimized the fractional order accumulation. Some numerical examples in Wu research and the real case in international visitor forecasting to Quang Ninh province were used to illustrate the effectiveness of proposed systematic approach compared with the GMr (1, 1) model and the traditional grey model. The simulation results demonstrate that the proposed systematic approach can have a significant improve the predication performance with the lowest error of MAPE in all cases. This result offers more valuable tools for policymakers in forecast, orientation and development in the future. In particular that for Quang Ninh policymakers in building and developing policies regarding tourism industry management.vi_VN
dc.language.isoenvi_VN
dc.publisherSpringer Naturevi_VN
dc.subjectEnhanced the GMr (1, 1) Model in Forecasting and Decision—Makingvi_VN
dc.subjectProposed systematic approach can have a significant improve the predication performance with the lowest error of MAPE in all casesvi_VN
dc.titleEnhanced the GMr (1, 1) Model in Forecasting and Decision—Makingvi_VN
dc.typeWorking Papervi_VN
Appears in Collections:CITA 2024 (International)

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