Forecast of the output value of Taiwan's IC industry using the Grey forecasting model

Lin, Chin‐Tsai; Yang, Shih-Yu

doi:10.1504/ijcat.2004.003657

Cited by 34 publications

(28 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Decision makers can apply the slicing machine sensors to costlessly balance the main effect factor and the noise factors. By using the concept of yin and yang variation and collocating grey forecasting, GM (1, 1) model (Deng, 1989 and2003;Lin and Lin, 2001;Hsieh, et al, 2002;Lin and Yang, 2003), precise drift and quality control from the slicing machine can be constantly monitored and forecast. If the GM (1, 1) model can accurately predict Bow value, then the main effect factor, noise factors and parameter settings are balanced and the slicing process is controlled.…”

Section: Relative Management and Controlmentioning

confidence: 99%

“…The following illustration thoroughly describes the method used to construct the model adopted herein by creating a sequence of one order linear moving GM (1,1). Figure 2 of stage 3 presents the first order differential equation of GM (1,1) model and the algorithms of the method are as follows (Deng, 2003;Lin and Yang, 2003;Lin et al, 2005):…”

Section: Relative Management and Controlmentioning

confidence: 99%

See 1 more Smart Citation

Relative Control and Management Philosophy

Chang¹

2006

Manufacturing the Future

View full text Add to dashboard Cite

Section: Relative Management and Controlmentioning

confidence: 99%

Section: Relative Management and Controlmentioning

confidence: 99%

Relative Control and Management Philosophy

Chang¹

2006

Manufacturing the Future

View full text Add to dashboard Cite

“…It focuses on models of uncertainty with insufficient information, predicts and makes decisions by conditional analyzing [9]. A system is defined as a white system if the information in it is known.…”

Section: Introductionmentioning

confidence: 99%

Dynamical randomicity and predictive analysis in cubic chaotic system

Zhang

Wang

2010

Nonlinear Dyn

View full text Add to dashboard Cite

The dynamical randomicity and grey prediction in cubic chaotic system will be studied in this paper. These stochastic symbolic sequences bear three features. The distribution of frequency, interoccurrence times, the first passage time, the rth passage time and the ordinal passage time are discussed separately. By using transfer probability of Markov chain (MC), one obtains analytic expressions of generating functions in three-probabilities stochastic wander model, which can be applied to all three symbolic systems. The visitation density function of cubic map will also be resolved. Especially, after introducing grey system theory, one is mainly using GM(1,1) model to forecast data sequences, and the usual forecast precision is approximately 90%. In the symbolic prediction of cubic chaotic dynamical system, the pre-Y. Zhang ( ) cision of grey prediction certainly will decrease as the length of symbolic sequence is increasing. But in this place we have found a generating rule that may realize chaotic synchronization at least in short and medium terms, and we can analyze and forecast in this way.

show abstract

“…Developed by Deng (1982Deng ( , 1989, the GM model is specifically designed for cases in which only limited data are available for forecasting and where the system environment is poorly defined and only partially understood, and has enjoyed success for numerous types of forecasting (Hsu 2001;Hsu and Wang 2002;Lin and Yang 2003;Wang and Hung 2003). For example, Li (2003) applied the grey prediction model to the global integrated circuit industry, and found that it may be more appropriate for short-term prediction than mid-and long-term prediction.…”

Section: Introductionmentioning

confidence: 99%

Modified Grey Model for Estimating Traffic Tunnel Air Quality

Lee

Wan

Kuo

et al. 2007

Environ Monit Assess

View full text Add to dashboard Cite

This study compared three forecasting models based on the mean absolute percentage errors (MAPE) of their accuracy in forecasting air pollution in a traffic tunnel: the Grey model (GM), the combination model used four sample point and five sample point prediction with GM (1,1)(GM(1,1)(4 + 5)), and the modified grey model (MGM). An MGM was combined using the four points of the original sequence using the original grey prediction GM (1,1) for short-term forecasting. The proposed method cannot only enhance the prediction accuracy of the original grey model, but can also solve the jump data forecasting problem something for which the original grey model is inappropriate. The MAPE was applied to the models, and the MGM found the proposed method to be simple and efficient. The MAPE of MGM, calculated over 3 h of forecasts, were as follows: 10.12 (Upwind), 10.07 (Middle) and 7.68 (Downwind) for CO; 10.79 (Upwind), 6.05 (Middle) and 5.98 (Downwind) for NOx, and 11.67 (Upwind), 7.32 (Middle) and 4.56 (Downwind) for NMHC. The MGM model results reveal that the combined forecasts can significantly decrease the overall forecasting error. Results of this demonstrate that MGM can accurately forecast air pollution in the Kaohsiung Chung-Cheng Tunnel.

show abstract

Forecast of the output value of Taiwan's IC industry using the Grey forecasting model

Cited by 34 publications

References 4 publications

Relative Control and Management Philosophy

Relative Control and Management Philosophy

Dynamical randomicity and predictive analysis in cubic chaotic system

Modified Grey Model for Estimating Traffic Tunnel Air Quality

Contact Info

Product

Resources

About