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 Studying on the Accuracy Improvement of GM (1, 1) Model
Tác giả hoặc Nhóm tác giả: Van Đat Nguyen, Van-Thanh Phan, Ngoc Thang Nguyen, Doan Nhan Dao & Le Thanh Ha
Nơi đăng: Communications in Computer and Information Science book series; Số: (CCIS,volume 1287);Từ->đến trang: pp 110–121;Năm: 2020
Lĩnh vực: Kinh tế; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
In order to expand the application of GM (1, 1) in the condition of fluctuation data and incomplete information, this paper proposed the new systematic optimization based on three steps as follows. First step, we used parameters c1 to transform any sequence data into the non-negative sequence data. The second, we used moving average operation method on the new sequence data to smooth the sequence data aim to satisfy the quasi-exponential condition and quasi-smooth condition. The final, we adopt Fourier series to modify residual error of model a grey sequence. To demonstrate the superiority of the proposed model, the numerical example in the research of Wang and Hsu and the raw data sequence are used. The simulation outcomes show that the proposed approach provides a better forecast results than several different kinds of grey forecasting models with the lowest average of MAPE for in and out-of-samples in two cases. For future direction, the author will applied different methodologies to improve the performance of GM (1, 1) or use proposed model to analyze the issues with high fluctuation data.
ABSTRACT
In order to expand the application of GM (1, 1) in the condition of fluctuation data and incomplete information, this paper proposed the new systematic optimization based on three steps as follows. First step, we used parameters c1 to transform any sequence data into the non-negative sequence data. The second, we used moving average operation method on the new sequence data to smooth the sequence data aim to satisfy the quasi-exponential condition and quasi-smooth condition. The final, we adopt Fourier series to modify residual error of model a grey sequence. To demonstrate the superiority of the proposed model, the numerical example in the research of Wang and Hsu and the raw data sequence are used. The simulation outcomes show that the proposed approach provides a better forecast results than several different kinds of grey forecasting models with the lowest average of MAPE for in and out-of-samples in two cases. For future direction, the author will applied different methodologies to improve the performance of GM (1, 1) or use proposed model to analyze the issues with high fluctuation data.
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