Forecast combination using grey prediction with fuzzy integral and time-varying weighting in tourism

Hu, Yi‐Chung

doi:10.1108/gs-04-2023-0037

Cited by 4 publications

(4 citation statements)

References 82 publications

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“…Compared to the integer order, the fractional order can better reveal the intrinsic characteristics and behaviors of objects. Therefore, the grey prediction model based on fractional-order accumulation has gradually attracted the attention of researchers and has been applied in various fields [35][36][37].…”

Section: Grey Models 131 Basic Principles Of Grey Forecasting Modelsmentioning

confidence: 99%

Dynamic Fractional-Order Grey Prediction Model with GWO and MLP for Forecasting Overseas Talent Mobility in China

Wu,

Fu,

Jiang

et al. 2024

Fractal Fract

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International students play a crucial role in China’s talent development strategy. Thus, predicting overseas talent mobility is essential for formulating scientifically reasonable talent introduction policies, optimizing talent cultivation systems, and fostering international talent cooperation. In this study, we proposed a novel fractional-order grey model based on the Multi-Layer Perceptron (MLP) and Grey Wolf Optimizer (GWO) algorithm to forecast the movement of overseas talent, namely MGDFGM(1,1). Compared to the traditional grey model FGM(1,1), which utilizes the same fractional order at all time points, the proposed MGDFGM(1,1) model dynamically adjusts the fractional-order values based on the time point. This dynamic adjustment enables our model to better capture the changing trends in the data, thereby enhancing the model’s fitting capability. To validate the effectiveness of the MGDFGM(1,1) model, we primarily utilize Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as the evaluation criteria for the prediction accuracy, as well as standard deviation (STD) as an indicator of the model stability. Furthermore, we perform experimental analysis to evaluate the predictive performance of the MGDFGM(1,1) model in comparison to NAÏVE, ARIMA, GM(1,1), FGM(1,1), LSSVR, MLP, and LSTM. The research findings demonstrate that the MGDFGM(1,1) model achieves a remarkably high level of prediction accuracy and stability for forecasting overseas talent mobility in China. The implications of this study offer valuable insights and assistance to government departments involved in overseas talent management.

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Section: Grey Models 131 Basic Principles Of Grey Forecasting Modelsmentioning

confidence: 99%

Dynamic Fractional-Order Grey Prediction Model with GWO and MLP for Forecasting Overseas Talent Mobility in China

Wu,

Fu,

Jiang

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

show abstract

“…It has been more than 40 years since grey system theory was first proposed by Professor Deng in 1982 (Deng, 1982), and it is now widely used in many fields such as agriculture, industry, water conservancy, economy and energy (Luo and Li, 2023;Tulkinov, 2023;Zhao et al, 2023;Hu, 2023). The Grey Forecasting Theory, as the core system of grey system theory, has achieved a series of important findings after years of research and development (Zhang et al, 2023;Sapnken, 2023;Li et al, 2023a;Wei et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Damping accumulated discrete MGM(1, m) power model and its application to forecasting agricultural output value share and employment share

Li,

Luo

2024

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PurposeThe damping accumulated discrete MGM(1, m) power model is proposed for the problem of forecasting the share of agricultural output value and the share of employment in China.Design/methodology/approachIn this study, the damping accumulated discrete MGM(1, m) power model was developed based on the idea of discrete modelling by introducing a damping accumulated generating operator and power index. The new model can better identify the non-linear characteristics existing between different factors in the multivariate system and can accurately describe and forecast the trend of changes between data series and each of them.FindingsThe validity and rationality of the new model are verified through numerical experiment. It is forecasted that in 2023, the share of agricultural output value in China will be 7.14% and the share of agricultural employment will be 21.98%, with an overall decreasing trend.Practical implicationsThe simultaneous decline in the share of agricultural output value and the share of employment is a common feature of countries that have achieved agricultural modernisation. Accurate forecasts of the share of agricultural output value and the share of employment can provide an important scientific basis for formulating appropriate agricultural development targets and policies in China.Originality/valueThe new model proposed in this study fully considers the importance of new information and has higher stability. The differential evolutionary algorithm was used to optimise the model parameters.

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“…This theory provides a feasible solution for analysis and decisionmaking problems under the condition of small data sets and is widely used in different application fields, such as engineering, manufacturing, business, etc. [7][8][9][10][11][12][13]. Grey modeling is an important branch of grey system theory and is mainly used for the extraction of pattern trends from small data sets [14].…”

Section: Introductionmentioning

confidence: 99%

“…Here, the absolute error (AE) is used as an indicator to measure the residual. If x (0) (k) is the actual observation and x(0) (k) is the value fitted by the model, then the calculation formula of AE is as Equation (10).…”

mentioning

confidence: 99%

Utilizing a Fractional-Order Grey Model to Predict the Development Trends of China’s Electronic Commerce Service Industry

Guo,

Chang,

Huang

2024

Fractal Fract

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Electronic commerce plays a vital role in the digital age, and the creation of a good electronic commerce ecosystem is crucial to maintaining economic growth. The electronic commerce service industry is a leading indicator of electronic commerce development, and its possible changes imply the future trends and innovation directions of the electronic commerce industry. An accurate grasp of the possible future revenue scale of the electronic commerce service industry can provide decision-making information for government policy formulation. Electronic commerce companies must formulate operational plans based on the latest information to determine strategic directions that are reasonable and consistent with the actual situation. Although there exist many prediction methods, they often fail to produce ideal results when the number of observations is insufficient. The fractional-order grey model is a common method used to deal with small data set prediction problems. This study therefore proposes a new modeling procedure for the fractional-order grey model to predict the revenue scale of China’s electronic commerce service industry. The results of experiments demonstrate that the proposed procedure can yield robust outputs under the condition of small data sets to reduce decision-making risks. Therefore, it can be regarded as a practical small data set analysis tool for managers.

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Forecast combination using grey prediction with fuzzy integral and time-varying weighting in tourism

Cited by 4 publications

References 82 publications

Dynamic Fractional-Order Grey Prediction Model with GWO and MLP for Forecasting Overseas Talent Mobility in China

Dynamic Fractional-Order Grey Prediction Model with GWO and MLP for Forecasting Overseas Talent Mobility in China

Damping accumulated discrete MGM(1, m) power model and its application to forecasting agricultural output value share and employment share

Utilizing a Fractional-Order Grey Model to Predict the Development Trends of China’s Electronic Commerce Service Industry

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