scholarly journals Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

2016 ◽  
Vol 51 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Yu Lei ◽  
Min Guo ◽  
Danning Zhao ◽  
Hongbing Cai ◽  
Dandan Hu
Keyword(s):  
High Reliability ◽  
Equation Model ◽  
Universal Time ◽  
Grey Model ◽  
Prediction Methods ◽  
Short Term ◽  
Differential Equation Model ◽  
Original Dataset ◽  
Grey Differential Equation ◽  
Good Forecast

Abstract A mathematical model known as one-order one-variable grey differential equation model GM(1, 1) has been herein employed successfully for the ultra short-term (<10days) predictions of universal time (UT1-UTC). The results of predictions are analyzed and compared with those obtained by other methods. It is shown that the accuracy of the predictions is comparable with that obtained by other prediction methods. The proposed method is able to yield an exact prediction even though only a few observations are provided. Hence it is very valuable in the case of a small size dataset since traditional methods, e.g., least-squares (LS) extrapolation, require longer data span to make a good forecast. In addition, these results can be obtained without making any assumption about an original dataset, and thus is of high reliability. Another advantage is that the developed method is easy to use. All these reveal a great potential of the GM(1, 1) model for UT1-UTC predictions.

Predicting Hardness of Four Groundwater Monitoring Stations in Kaohsiung City of Taiwan Using Seven Types of GM (1, 1) Model

2014 ◽  
Vol 905 ◽  
pp. 314-317
Author(s):  
Tzu Yi Pai ◽  
Ray Shyan Wu ◽  
Ching Ho Chen ◽  
Li Chen ◽  
Ching Yuan Lin ◽  
...  
Keyword(s):  
Groundwater Monitoring ◽  
Competent Authority ◽  
Equation Model ◽  
Percentage Error ◽  
Differential Equation Model ◽  
Predicting Performance ◽  
Grey Differential Equation ◽  
The Mean ◽  
Kaohsiung City ◽  
Monitoring Stations

In this study, seven types of first-order and one-variable grey differential equation model (abbreviated as GM (1, 1) model) were used to predict the hardness of four groundwater monitoring stations in Kaohsiung City of Taiwan. The mean absolute percentage error (MAPE) was used to evaluate the predicting performance. The results indicated the minimum MAPE of 4.71 %, 3.15 %, 2.66 %, and 16.63 % could be achieved when predicting hardness of Fonsi, Datung, Shaukang, and Chihsien stations, respectively. According to the results, it revealed that GM (1, 1) was an efficiently early warning tool for providing groundwater quality information to the competent authority.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Che-Jung Chang ◽  
Der-Chiang Li ◽  
Wen-Li Dai ◽  
Chien-Chih Chen
Keyword(s):  
Semiconductor Manufacturing ◽  
Manufacturing System ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey System ◽  
Wafer Level ◽  
Short Term ◽  
Packaging Process ◽  
Wafer Level Packaging

The wafer-level packaging process is an important technology used in semiconductor manufacturing, and how to effectively control this manufacturing system is thus an important issue for packaging firms. One way to aid in this process is to use a forecasting tool. However, the number of observations collected in the early stages of this process is usually too few to use with traditional forecasting techniques, and thus inaccurate results are obtained. One potential solution to this problem is the use of grey system theory, with its feature of small dataset modeling. This study thus uses the AGM(1,1) grey model to solve the problem of forecasting in the pilot run stage of the packaging process. The experimental results show that the grey approach is an appropriate and effective forecasting tool for use with small datasets and that it can be applied to improve the wafer-level packaging process.

Short-term prediction of UT1-UTC by combination of the grey model and neural networks

2017 ◽  
Vol 59 (2) ◽  
pp. 524-531 ◽  
Author(s):  
Yu Lei ◽  
Min Guo ◽  
Dan-dan Hu ◽  
Hong-bing Cai ◽  
Dan-ning Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Grey Model ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

Short-term prognosis in acute myocardial infarction: Evaluation of different prediction methods

1984 ◽  
Vol 107 (6) ◽  
pp. 1241-1251 ◽  
Author(s):  
Erling Birk Madsen ◽  
Elizabeth Gilpin ◽  
Hartmut Henning
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Prediction Methods ◽  
Short Term

scholarly journals A spatially varying stochastic differential equation model for animal movement

2018 ◽  
Vol 12 (2) ◽  
pp. 1312-1331 ◽  
Author(s):  
James C. Russell ◽  
Ephraim M. Hanks ◽  
Murali Haran ◽  
David Hughes
Keyword(s):  
Differential Equation ◽  
Stochastic Differential Equation ◽  
Animal Movement ◽  
Equation Model ◽  
Differential Equation Model ◽  
Spatially Varying

scholarly journals A Stochastic TB Model for a Crowded Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Sibaliwe Maku Vyambwera ◽  
Peter Witbooi
Keyword(s):  
Compartmental Model ◽  
Deterministic Model ◽  
Reproduction Number ◽  
Stochastic Perturbation ◽  
Equation Model ◽  
Ordinary Differential Equation Model ◽  
Differential Equation Model ◽  
The Stability ◽  
Crowded Environments ◽  
Disease Free

We propose a stochastic compartmental model for the population dynamics of tuberculosis. The model is applicable to crowded environments such as for people in high density camps or in prisons. We start off with a known ordinary differential equation model, and we impose stochastic perturbation. We prove the existence and uniqueness of positive solutions of a stochastic model. We introduce an invariant generalizing the basic reproduction number and prove the stability of the disease-free equilibrium when it is below unity or slightly higher than unity and the perturbation is small. Our main theorem implies that the stochastic perturbation enhances stability of the disease-free equilibrium of the underlying deterministic model. Finally, we perform some simulations to illustrate the analytical findings and the utility of the model.

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