Prediction of Silkworm Cocoon Yield in China Based on Grey-Markov Forecasting Model

Huang, Lingxia; Jin, Peng; He, Yong; Chengfu, Lou; Huang, Min; Chen, Mingang

doi:10.1007/11925231_47

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…They do get more accuracy on their prediction. Also, Huang et al [11] predict the silkworm cocoon yield in china base on the grey-Markov forecasting model. The findings show that grey-Markov has more accuracy than other forecasting model.…”

Section: Grey System Theorymentioning

confidence: 99%

Analyzing Taiwan IC Assembly Industry by Grey-Markov Forecasting Model

2013

Mathematical Problems in Engineering

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This study utilizes the black swan theorem to discuss how to face the lack of historical data and outliers. They may cause huge influences which make it impossible for people to predict the economy from their knowledge or experiences. Meanwhile, they cause the general dilemma of which prediction tool to be used which is also considered in this study. For the reason above, this study uses 2009 Q1 to 2010 Q4 quarterly revenue trend of Taiwan’s semiconductor packaging and testing industry under the global financial turmoil as basis and the grey prediction method to deal with nonlinear problems and small data. Under the lack of information and economic drastic changes, this study applies Markov model to predict the industry revenues of GM(1,1) and DGM(1,1) results. The results show that the accuracy of 2010 Q1–Q3 is 88.37%, 90.27%, sand 91.13%, respectively. Besides, they are better than the results of GM(1,1) and DGM(1,1) which are 86.51%, 77.35%, 75.46% and 73.77%, 74.25%, 59.72%. The results show that the prediction ability of the grey prediction with Markov model is better than traditional GM(1,1) and DGM(1,1) sfacing the changes of financial crisis. The results also prove that the grey-Markov chain prediction can be the perfect criterion for decision-makers judgment even when the environment has undergone drastic changes which bring the impact of unpredictable conditions.

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Section: Grey System Theorymentioning

confidence: 99%

Analyzing Taiwan IC Assembly Industry by Grey-Markov Forecasting Model

2013

Mathematical Problems in Engineering

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“…As the results suggest, carbon emissions in Jiangsu Province would reach nearly 334 million tons in 2020, and there would have been a rapid growth in carbon emissions in 2020. Huang and Shang (2015) built the traditional Grey Markov prediction model and improved the actual error of the initial prediction of the Grey Markov prediction. The improved new model was adopted to calculate carbon emissions in China.…”

Section: Introductionmentioning

confidence: 99%

Analysis of influencing factors of Chinese provincial carbon emissions based on projection pursuit model and Markov transfer matrix

Wen

Huang

2019

IJCCSM

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Purpose Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance. Design/methodology/approach This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix. Findings The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications. Originality/value This paper provides an insight into the current state and the future changes in carbon emissions.

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A Grey-Markov predication for unemployment rate of graduates in China

Chen

2009

2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009)

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Influenced by a number of uncertain factors, Unemployment rate of college graduates has characteristics of stochastic nature and fluctuations. Single gray GM (1,1)model could not fit these data better, and its forecasting accuracy is lower. However, Markov forecast can make up for this deficiency. Drawing on the virtue of gray model and Markov forecasting Model, this paper presents a Grey-Markov forecasting model for forecasting unemployment rate of graduates in china. This method takes into account the general trend series and random fluctuations about this trend. It has the merits of both simplicity of application and high forecasting precision. This paper is on basis of historical data of the unemployment rate of graduates in China, and forecasts and analyzes the unemployment rate of graduates in China in terms of Grey-Markov forecasting model.

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Prediction of Silkworm Cocoon Yield in China Based on Grey-Markov Forecasting Model

Cited by 3 publications

References 4 publications

Analyzing Taiwan IC Assembly Industry by Grey-Markov Forecasting Model

Analyzing Taiwan IC Assembly Industry by Grey-Markov Forecasting Model

Analysis of influencing factors of Chinese provincial carbon emissions based on projection pursuit model and Markov transfer matrix

A Grey-Markov predication for unemployment rate of graduates in China

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