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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Minh Ly Duc
Faculty of Commerce, Van Lang University, Ho Chi Minh City, Vietnam
Computer Science & IT

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A Multi-Model Time Series Framework for Forecasting Vietnam’s Tourism Revenue in the Post-COVID Recovery Era Ho Nhat Hiep; Nguyen Ngoc Xuan Quynh; Nguyen Thi Van Anh; Minh Ly Duc
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16045

Abstract

This study forecasts Vietnam’s tourism revenue in the post-COVID-19 recovery period using a multi-model time series framework. The dataset includes three groups (ID 292–294) covering tourism business performance, economic sectors, and regional revenue. Six forecasting models are applied and evaluated using MAPE, MAD, and MSD. Results show that decomposition and Holt–Winters achieve the best accuracy (e.g., MAPE as low as 18%), while moving average performs well in specific cases (MAPE ≈ 28%). Forecasts indicate that tourism revenue may nearly double by 2030, driven mainly by domestic demand and the non-state sector, although international tourism recovers more slowly.
Co-Authors Ho Nhat Hiep Nguyen Ngoc Xuan Quynh Nguyen Thi Van Anh