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2020 | OriginalPaper | Chapter

Studying on the Accuracy Improvement of GM (1, 1) Model

Authors : Van Đat Nguyen, Van-Thanh Phan, Ngoc Thang Nguyen, Doan Nhan Dao, Le Thanh Ha

Published in: Advances in Computational Collective Intelligence

Publisher: Springer International Publishing

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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 analyse the issues with high fluctuation data.

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Metadata
Title
Studying on the Accuracy Improvement of GM (1, 1) Model
Authors
Van Đat Nguyen
Van-Thanh Phan
Ngoc Thang Nguyen
Doan Nhan Dao
Le Thanh Ha
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-63119-2_10

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