Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging

Chang, Che-Jung; Li, Der-Chiang; Dai, Wen Li; Chen, Chien Chih

doi:10.1155/2013/526806

Cited by 14 publications

(13 citation statements)

References 21 publications

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“…When the original data series are acquired, the AGO series can be built. A detailed building of AGO series was performed by Chang et al 45 Its first-order differential equation can be expressed as follows…”

Section: Grey Prediction Modelmentioning

confidence: 99%

“…Both the developing coefficient a and the grey controlled variable b were calculated using the ordinary least-square method. The parameters a and b can be calculated through the accumulated matrix and a detailed description of accumulated matrix was performed by Chang et al 45 The values of a and b were then input into equation (7) to discretize the differential equation. The desired prediction output at time K + 1 can be estimated by an inverse AGO, which was determined by the following relation…”

Section: Grey Prediction Modelmentioning

confidence: 99%

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An improved fractal prediction model for forecasting mine slope deformation using GM (1, 1)

Dong

Shi

et al. 2015

Structural Health Monitoring

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The forecasting slope deformation potential is required to evaluate slope safety during open-pit mining, allowing us to formulate and promote effective emergency strategies in advance to prevent slope failure disasters. Although fractal models have been used to predict slope deformation, such limitations as low prediction accuracy, poor stability and the requirement for large amounts of data must be overcome. This article proposes an improved fractal model to forecast mine slope deformation using the grey system theory. The GM (1, 1) model is used in the improved fractal model to optimize the fitting function of the fractal dimension because of its high computational efficiency and strong fitting ability. Data sequences spanning 13 days from 11 global positioning system monitoring stations in the Jinduicheng open-pit mine in Shaanxi Province, China, were applied to forecast the slope deformation. The results from both the traditional fractal model and the improved fractal model can accurately forecast the slope deformation value fairly close to the actual field monitoring value, but the latter can make a more accurate prediction than the former. There is a significant relationship between the prediction accuracy and the data sequence dispersion. Further analysis revealed that our improved fractal model is more capable of resisting the volatility existing in the data sequences than the traditional fractal model. These findings assist in understanding the applicability of prediction models and the deformation trends of open-pit mine slopes.

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Section: Grey Prediction Modelmentioning

confidence: 99%

Section: Grey Prediction Modelmentioning

confidence: 99%

An improved fractal prediction model for forecasting mine slope deformation using GM (1, 1)

Dong

Shi

et al. 2015

Structural Health Monitoring

View full text Add to dashboard Cite

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“…This study used these four methods to examine the accuracy of a forecasting model. 12,30,31 Each error type was defined as follows: …”

Section: Ngbmmentioning

confidence: 99%

Using modified grey forecasting models to forecast the growth trends of green materials

Tsai

Lee

Guo

2013

Proceedings of the Institution of Mechanical Engineers, Part B:

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The use of green materials reflects the notion that production materials should generate minimal environmental pollution through various efforts, including the design and development of low-pollution materials, innovation and improvement of low-pollution manufacturing processes, and recycling and reuse of materials. The development of green materials is an important part of corporate social responsibility. Companies need to use resources legitimately and have environmental protection responsibility. Forecasting the growth trends of green copper clad laminate material is crucial for manufacturers of printed circuit boards and green copper clad laminates. The main purpose of this study was to forecast the growth trends of green electronic materials. The industry sample investigated in this study only numbered 14.Because of the limited sample of historical data, the data distribution did not exhibit a normal distribution. Prediction methods for large data sets were not suitable for this study. Thus, three grey forecasting models (i.e. the grey model(1,1), non-linear grey Bernoulli model (1,1), and grey Verhulst model) were adopted for theoretical derivation and scientific verification. The results yielded by these methods were compared with the results of regression analysis to verify the forecasting accuracy and suitability of the three methods. The results indicated that for small data sets, the forecasting accuracy of the non-linear grey Bernoulli model(1,1) and grey Verhulst model was superior to that of the original grey model(1,1) as well as the regression analysis method.

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“…This exploratory research on uncertainty issues can be approximately categorized as the study of random phenomenon (Berry and Lindgren, 1996), cognitive indetermination (Kilr and Yuan, 1995), and insufficient information from a deficient sample size (Liu and Lin, 2006;Chang et al, 2013). The major problem that is encountered in the early stage of a manufacturing system is the informational uncertainty due to the limited number of samples (Ho, 2013).…”

Section: Introductionmentioning

confidence: 99%

A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting

Chang

Dai

Chen

2015

Journal of the Operational Research Society

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Small-data-set forecasting problems are a critical issue in various fields, with the early stage of a manufacturing system being a good example. Manufacturers require sufficient knowledge to minimize overall production costs, but this is difficult to achieve due to limited number of samples available at such times. This research was thus conducted to develop a modelling procedure to assist managers or decision makers in acquiring stable prediction results from small data sets. The proposed method is a two-stage procedure. First, we assessed some single models to determine whether the tendency of a real sequence can be reflected using grey incidence analysis, and we then evaluated their forecasting stability based on the relative ratio of error range. Second, a grey silhouette coefficient was developed to create an applicable hybrid forecasting model for small samples. Two real cases were analysed to confirm the effectiveness and practical value of the proposed method. The empirical results showed that the multimodel procedure can minimize forecasting errors and improve forecasting results with limited data. Consequently, the proposed procedure is considered a feasible tool for small-data-set forecasting problems.

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Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging

Cited by 14 publications

References 21 publications

An improved fractal prediction model for forecasting mine slope deformation using GM (1, 1)

An improved fractal prediction model for forecasting mine slope deformation using GM (1, 1)

Using modified grey forecasting models to forecast the growth trends of green materials

A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting

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