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Analysing the high-tech industry with a multivariable grey forecasting model based on fractional order accumulation
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Analysing the high-tech industry with a multivariable grey forecasting model based on fractional order accumulation
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Journal Article
Analysing the high-tech industry with a multivariable grey forecasting model based on fractional order accumulation
2019
Overview
Purpose
High-tech industries play an important role in promoting economic and social development. The purpose of this paper is to accurately predict and analyze the output value of high-tech products in Guangdong Province, China, by using a multivariable grey model.
Design/methodology/approach
Based on the principle of fractional order accumulation, this study proposes a multivariable grey prediction model. To further enhance the prediction ability and accuracy of the model, an optimized model is established by reconstructing the background value. The optimal parameters are solved by minimizing the average relative error of the system characteristic sequence with the constraint of parameter relationships.
Findings
The results from the study show that the two proposed models exhibit better simulation and prediction performance than the traditional models, while the optimized model can significantly improve the modelling precision. In addition, it is predicted that the output value of high-tech products is 12,269.443bn yuan in 2021, which will approximately double from 2016 to 2021.
Research limitations/implications
The two proposed models can be used to forecast the trend of the system and are grown as an effective extension and supplement of the traditional multivariable grey forecasting models.
Practical implications
The forecast and analysis of the development prospects of high-tech industries would be useful for the government departments of Guangdong Province and professional forecasters to grasp the future of high-tech industries and formulate decision planning.
Originality/value
A new multivariable grey prediction model based on fractional order accumulation and its optimized model obtained by reconstructing the background value, which can improve the modelling accuracy of the traditional model, is proposed in this paper.
Publisher
Emerald Publishing Limited,Emerald Group Publishing Limited
Subject
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