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 Forecasting the Coffee Consumption Demand in Vietnam Based on Grey Forecasting Model
Tác giả hoặc Nhóm tác giả: Ngoc Thang Nguyen, Van-Thanh Phan, Van Ðat Nguyen, Thanh Ha Le, and Thao Vy Pham
Nơi đăng: Vietnam Journal of Computer Science; Số: Vol. 09, No. 03;Từ->đến trang: pp. 245-259;Năm: 2022
Lĩnh vực: Kinh tế; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Forecasting the domestic coffee consumption demand is important for policy planning and making the right decisions. Thus, in this study, we try to find out the most suitable model among three proposed models (GM (1,1), DGM (1,1) and Grey Verhulst model (GVM)) for predicting the amount of domestic coffee consumption in Vietnam in the future. Yearly data of coffee consumption from 2010–2020 are used in this research. The experimental results indicated that the GM (1,1) is the most accurate model selected in this study with the lowest average value of δ=2.93%. So, the GM (1,1) model is strongly suggested in the analysis of coffee consumption demand in Vietnam. Finding the right tool will help managers make right decisions easily for sustainable development of the coffee industry in Vietnam in the future.
ABSTRACT
Forecasting the domestic coffee consumption demand is important for policy planning and making the right decisions. Thus, in this study, we try to find out the most suitable model among three proposed models (GM (1,1), DGM (1,1) and Grey Verhulst model (GVM)) for predicting the amount of domestic coffee consumption in Vietnam in the future. Yearly data of coffee consumption from 2010–2020 are used in this research. The experimental results indicated that the GM (1,1) is the most accurate model selected in this study with the lowest average value of δ=2.93%. So, the GM (1,1) model is strongly suggested in the analysis of coffee consumption demand in Vietnam. Finding the right tool will help managers make right decisions easily for sustainable development of the coffee industry in Vietnam in the future.
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