Analyzing Taiwan IC Assembly Industry by Grey-Markov Forecasting Model

Li, Lin; Wu, Shan

doi:10.1155/2013/658630

Cited by 8 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gray-Markov model is a forecasting method integrating the Gray theory with the Markov theory [17][18][19][20][21][22][23][24][25]. Firstly, GM(1,1) is constructed to obtain the predicted residual value.…”

Section: Gray-markov Modelmentioning

confidence: 99%

Using Gray-Markov Model and Time Series Model to Predict Foreign Direct Investment Trend for Supporting China’s Economic Development

Zheng¹,

Shu²,

Chen³

et al. 2019

Time Series Analysis - Data, Methods, and Applications

View full text Add to dashboard Cite

Foreign direct investment (FDI) is one of the important factors affecting China's economic development, the prediction of which is the basis of its development and decision-making. Based on elaborating the significant role in China's economic growth and the status quo of utilizing foreign investment over the period between 2000 and 2016, this chapter attempts to construct Gray-Markov model (GMM) and time series model (TSM) to forecast the trend of China's utilization of FDI and then compares the precision of two different prediction models to obtain a better one. Results indicate that although it is qualified, traditional Gray model needs to be optimized; GMM is built to help modify the result, improve Gray-related degrees, and narrow the gap with real value. Comparing the accuracy of GMM with that of TSM, we can conclude that the fitting effect of GMM is better. To increase the credibility of these results, this chapter is based on the data of Beijing and Chongqing from 1990 till 2016, also verifying that the fitting effect of GMM is superior to that of the TSM. Then, we can safely draw a conclusion that the prediction model of GMM is more credible, which has a certain referencing value for the utilization of FDI.

show abstract

Section: Gray-markov Modelmentioning

confidence: 99%

Using Gray-Markov Model and Time Series Model to Predict Foreign Direct Investment Trend for Supporting China’s Economic Development

Zheng¹,

Shu²,

Chen³

et al. 2019

Time Series Analysis - Data, Methods, and Applications

View full text Add to dashboard Cite

show abstract

“…[11–13] This model is a dynamic system which based on the state transition. [14] The system's state is randomized at all time and independent with prior states, this characteristic is called non-aftereffect property or Markov process. State transition probability matrix is the model's foundation.…”

Section: Introductionmentioning

confidence: 99%

Analyzing maternal mortality rate in rural China by Grey-Markov model

Wang

Shen²,

Jiang³

2019

Medicine

View full text Add to dashboard Cite

Maternal mortality rate (MMR) in China has reduced during a decade but still higher than many countries around the world. Rural China is the key region which affects over all maternal death. This study aims to develop a suitable model in forecasting rural MMR and offer some suggestions for rural MMR intervention. Data in this study were collected through the Health Statistical Yearbook (2017) which included the overall MMR in China and urban and rural mortality rate. A basic grey model (GM(1,1)), 3 metabolic grey models (MGM), and a hybrid GM(1,1)–Markov model were presented to estimate rural MMR tendency. Average relative error (ARE), the post-test ratio ( C ), and small error probability ( P ) were adopted to evaluate models’ fitting performance while forecasting effectiveness was compared by relative error. The MMR in rural China reduced obviously from 63.0 per 100,000 live births in 2005 to 21.1 per 100,000 live births in 2017. One basic GM(1,1) model was built to fit the rural MMR and the expression was X ^((1)) ( k + 1) = 553.80e^0.0947 k – 550.00 ( C = 0.0456, P > .99). Three MGM models expressions were X ^((1)) ( k + 1) = 548.67e^0.0923 k – 503.17 ( C = 0.0540, P > .99), X ^((1)) ( k + 1) = 449.39e^0.0887 k – 408.09 ( C = 0.0560, P > .99), X ^((1)) ( k + 1) = 461.33e^0.0893 k – 425.23( C = 0.0660, P > .99). Hybrid GM(1,1)–Markov model showed the best fitting performance ( C = 0.0804, P > .99). The relative errors of basic GM(1,1) model and hybrid model in fitting part were 2.42% and 2.03%, respectively, while 5.35% and 2.08%, respectively, in forecasting part. The average relative errors of MGM were 2.07% in fitting part and 17.37% in forecasting part. Data update was crucial in maintain model's effectiveness. The hybrid GM(1,1)–Markov model was better than basic GM(1,1) model in rural MMR prediction. It could be considered as a decision-making tool in rural MMR intervention.

show abstract

“…Chen (2011) presented a Grey Markov model to forecast financial crises for an enterprise [ 24 ]. Lin et al (2013) used a Grey-Markov forecasting model to analyze Taiwan’s IC assembly industry [ 25 ]. Kordnoori et al (2014) applied a Grey Markov model to analyze global ICT development [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation

Duan

Jiao

Qishan

et al. 2017

IJERPH

View full text Add to dashboard Cite

The sharp increase of the aging population has raised the pressure on the current limited medical resources in China. To better allocate resources, a more accurate prediction on medical service demand is very urgently needed. This study aims to improve the prediction on medical services demand in China. To achieve this aim, the study combines Taylor Approximation into the Grey Markov Chain model, and develops a new model named Taylor-Markov Chain GM (1,1) (T-MCGM (1,1)). The new model has been tested by adopting the historical data, which includes the medical service on treatment of diabetes, heart disease, and cerebrovascular disease from 1997 to 2015 in China. The model provides a predication on medical service demand of these three types of disease up to 2022. The results reveal an enormous growth of urban medical service demand in the future. The findings provide practical implications for the Health Administrative Department to allocate medical resources, and help hospitals to manage investments on medical facilities.

show abstract

Analyzing Taiwan IC Assembly Industry by Grey-Markov Forecasting Model

Cited by 8 publications

References 8 publications

Using Gray-Markov Model and Time Series Model to Predict Foreign Direct Investment Trend for Supporting China’s Economic Development

Using Gray-Markov Model and Time Series Model to Predict Foreign Direct Investment Trend for Supporting China’s Economic Development

Analyzing maternal mortality rate in rural China by Grey-Markov model

Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation

Contact Info

Product

Resources

About