Forecasting Guangdong’s Marine Science and Technology, Marine Economy, and Employed Persons by Coastal Regions—Based on Rolling Grey MGM(1,m) Model

Shan, Xin; Cao, Yun

doi:10.3390/w14050824

Cited by 7 publications

(6 citation statements)

References 29 publications

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“…By using (12) instead of the original sequence in (1), the increase of the new information weight can be achieved, makings the prediction accuracy further improved.…”

Section: B New Information Priority Rolling Grey Predictionmentioning

confidence: 99%

“…In order to achieve accurate multi-step prediction, a short-term rolling grey prediction combined with intelligent optimization algorithms is proposed in [11] to improve the accuracy of shortterm prediction. The multi-parameter rolling grey prediction MGM (1, m) model is used to achieve long-term rolling prediction of the coastal economy in Guangdong province [12]. New prediction information is continuously added to the original sequence to achieve long-term disaster rolling prediction in Taiwan [13].…”

Section: Introductionmentioning

confidence: 99%

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Current control of EAST Fast Control Power Supply Based on Improved Grey Prediction Variable Gain PI

Chen

Huang

Wang

2023

Preprint

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The primary performance index of the fast control power supply in the Experimental Advanced Superconducting Tokamak (EAST) is to quickly track the reference current signal, realize the excitation of the load coil with the output current, and feedback control the vertical displacement of the plasma. The current on the load coil of EAST fast control power supply is affected by various uncertain environmental factors, making it difficult to establish a standard mathematical model for prediction. Accurate object model is not required in grey prediction, and only a small amount of known information is needed to achieve short-term prediction of output current. Grey prediction has been studied and applied in EAST fast control power supply to some extent. To further improve prediction accuracy and accelerate output current response speed, an improved grey prediction algorithm is proposed to achieve output current prediction. Considering the control delay in digital control, the output current of the next period is predicted using the sampled original sequence. Following the principle of new information priority, an original sequence transformation operator is proposed to weight new information. The predicted output current in the next period is added to the original sequence while removing the oldest original sequence, to achieve rolling prediction of the output current in the next two periods. The control value of the output current is loaded one switching period in advance, further improving prediction accuracy while compensating for control delay. The output gain of proportional integral (PI) control is adaptively adjusted based on the error between the predicted current and the reference current, and the improved grey prediction variable gain PI control achieves fast and accurate control of the output current. Simulation and experimental results show that the proposed control method has high prediction accuracy. Compared to traditional PI control and grey prediction control, the proposed control method can effectively improve the output current response speed.

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“…By using (12) instead of the original sequence in (1), the increase of the new information weight can be achieved, makings the prediction accuracy further improved.…”

Section: B New Information Priority Rolling Grey Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Current control of EAST Fast Control Power Supply Based on Improved Grey Prediction Variable Gain PI

Chen

Huang

Wang

2023

Preprint

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“…In terms of marine economic prediction, most of the relevant literature uses grey models, time series models and VAR models to numerically forecast the GOP, the scale of various marine industries and the number of marine employees (Raman et al, 2017;Ma et al, 2020;Liu, 2020;Li et al, 2021a, b;Shan and Cao, 2022). Among above methods, GM(1,1) and ARIMA models are limited to univariate forecasting and cannot consider the influence relationship among multiple variables; GM(1,N) models can incorporate multiple variables into the model but only focus on the unidirectional impact of multiple variables on a single variable; VAR models can present the bidirectional effects between multiple variables but have high requirements on the length of data samples.…”

Section: Literature Reviewmentioning

confidence: 99%

“…, 2017; Ma et al. , 2020; Liu, 2020; Li et al ., 2021a, b; Shan and Cao, 2022). Among above methods, GM(1,1) and ARIMA models are limited to univariate forecasting and cannot consider the influence relationship among multiple variables; GM(1,N) models can incorporate multiple variables into the model but only focus on the unidirectional impact of multiple variables on a single variable; VAR models can present the bidirectional effects between multiple variables but have high requirements on the length of data samples.…”

Section: Introductionmentioning

confidence: 99%

Forecasting China’s marine scientific research and education, marine industrial structure upgrading and marine economy growth based on the AWBO-MGM(1,m) model

Xu¹,

Li²

2023

MAEM

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PurposeThe purpose of this paper is to explore the internal interaction mechanism of marine scientific research and education, industrial structure upgrading and marine economic growth from a systematic perspective, based on which this work forecasts their future development trends.Design/methodology/approachIn this study, a multivariate grey model is applied to the prediction of marine scientific research and education, industrial structure upgrading and marine economic growth. Considering the impact of the COVID-19 on marine development, this paper introduces the weakening buffer operator into MGM(1,m) and constructs the AWBO-MGM(1,m) model. To verify the validity and accuracy of the new model, this paper uses AWBO-MGM(1,m), MGM(1,m), GM(1,N), GM(1,1), back propagation neural network and linear regression models for simulation and prediction based on the data from 2010 to 2021, respectively.FindingsFrom the theoretical perspective, the development of marine scientific research and education can accelerate industrial upgrading and promote marine economic growth by providing high-quality talents, promoting marine science and technology progress and reducing transaction costs; while the upgrading of marine industrial structure and marine economic growth can promote the development of marine scientific research and education by guiding social capital, enhancing talent demand and stimulating market vitality. From the empirical analysis, the AWBO-MGM(1,m) model can effectively deal with epidemic shocks and has higher fitting and prediction accuracy than the other five comparative models.Practical implicationsThe government should pay attention to the construction of marine scientific research and education, so as to provide high-quality talents and advanced scientific research results for the high-quality development of marine economy. On the basis of using science and technology to firmly build the primary and secondary marine industries, the government should actively guide the labor, capital and other factors of production to the tertiary industry, thereby promoting the optimization and upgrading of marine industrial structure.Originality/valueOn the one hand, the interplay mechanism of marine scientific research and education, industrial structure upgrading and marine economic growth is analyzed from a systematic perspective; on the other hand, the enhanced AWBO-MGM(1,m) possesses higher forecasting performance and is applicable to the systemic multivariate forecasting problem in the presence of outstanding external shocks.

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“…These factors collectively contribute to establishing a dynamic equilibrium within the "economic-socialresource-environmental" system of human-sea interactions. The primary drivers behind the pursuit of high-quality development in the marine economy encompass capital, technology, labor, and advancements in technology (Shan and Cao, 2022). In relation to the comprehensive assessment of the high-quality advancement of the marine economy, certain scholars posit that the total factor productivity (TFP) of marine green sectors can partially reflect the extent of such development.…”

Section: Introductionmentioning

confidence: 99%

Research on the impact mechanism of scientific and technological innovation on the high-quality development of the marine economy

Feng,

Guan,

Wang

et al. 2024

Front. Mar. Sci.

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Promoting the high-quality development of the marine economy is an inevitable choice for building a maritime power, and technological innovation can provide strong impetus for the high-quality development of the marine economy. Therefore, it is urgent to clarify the impact mechanism of technological innovation on the high-quality development of the marine economy, and promote the high-quality development of the marine economy. This study employs panel data from 11 coastal provinces and municipalities (autonomous regions) in China, spanning the years 2006 to 2020. The objective is to empirically evaluate the mechanism through which scientific and technical innovation impacts the high-quality development of the marine economy. This is achieved by utilizing the PVAR model and the mediation effect model. The research findings indicate that there is a noteworthy impact of enhancing scientific and technological innovation on the marine economy of China. Moreover, there exists a significant reciprocal relationship between scientific and technological innovation and the pursuit of high-quality development in the marine economy. It is observed that scientific and technological innovation not only has a positive influence on the high-quality development of the marine economy by enhancing green total factor productivity and optimizing the industrial structure, but it also facilitates the advancement of the marine economy through the chain mediation path of “improving green total factor productivity and optimizing industrial structure”.

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Forecasting Guangdong’s Marine Science and Technology, Marine Economy, and Employed Persons by Coastal Regions—Based on Rolling Grey MGM(1,m) Model

Cited by 7 publications

References 29 publications

Current control of EAST Fast Control Power Supply Based on Improved Grey Prediction Variable Gain PI

Current control of EAST Fast Control Power Supply Based on Improved Grey Prediction Variable Gain PI

Forecasting China’s marine scientific research and education, marine industrial structure upgrading and marine economy growth based on the AWBO-MGM(1,m) model

Research on the impact mechanism of scientific and technological innovation on the high-quality development of the marine economy

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