Analysing the high-tech industry with a multivariable grey forecasting model based on fractional order accumulation

Abstract

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.

An Improved Nonhomogeneous Grey Model with Fractional-Order Accumulation and Its Application

“…Proof. Since s r (j) keeps continduous over [i − 1, i], there exist the maximum value D and minimum value d for s r (j) over [i − 1, i], which makes d ≤ s r (j) ≤ D hold true; then, we have φd ≤ φs r (i) ≤ φD, (12) which is also…”

“…As previous literature revealed, for the ability to analyse and process data sequences, grey-based models have been broadly applied among diverse disciplines because of their excellent implementation on small-scale sample modeling, such as natural gas consumption [2][3][4], electric power supply and demand [5][6][7][8], renewable energy [9,10], industry [11,12], and medicine [13,14]. Although grey-based models have their own advantages, there still exist some shortcomings.…”

The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

“…Grey prediction models play an important role in the grey system theory, which was pioneered by Deng [4]. At present, grey prediction models have been widely used in various fields of society due to their high prediction accuracy and the advantages of small sample modeling [5][6][7][8][9][10][11][12]. Depending on the number of variables required for modeling, grey prediction models can be divided into univariate grey prediction models and multivariable grey prediction models.…”

“…At present, scholars mainly focus on the improvement of the GM (1, 1) model, but there are few studies on the improvement of the GM (1, N) model. To fill this gap, Zeng et al established a new optimized grey prediction model and confirmed the feasibility and effectiveness of the model through examples [12]; in order to increase the adaptability of the multivariable grey prediction model, Wang established a multivariable grey prediction model with time power terms [13]; Xie et al established a discrete multivariate grey prediction model [14]; in order to further improve the adaptability of the discrete multivariable grey prediction model, Ding et al proposed a discrete multivariable grey prediction model with time power terms [15]; Considering that the discrete multivariable grey model has the problem of low model accuracy, Ding et al proposed a multivariate discrete grey prediction model with time delay effect [16]; Ma et al established a new multivariable grey prediction model with fractional order accumulation [17] and so on. ese improvements all further improved the accuracy of the multivariable grey prediction model and expanded the grey system theory.…”

Forecasting the Cumulative Confirmed Cases with the FGM and Fractional-Order Buffer Operator in Different Stages of COVID-19

“…In order to improve the prediction accuracy of limited data with nonexponential growth trends, many scholars derived more models from traditional GM (1,1), such as the grey Verhulst model [11,12], grey Bernoulli model [13,14], and fractionalorder accumulation grey model (FGM) [15]. Since the fractional-order accumulation grey model solves the problem that the GM (1, 1) model cannot be used to predict the data that do not follow the grey exponential law completely accurately, it has attracted researchers' attention widely, such as Liu et al [16], Zeng [17], Wu et al [18], and Liu et al [19].…”