Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model

Huang, Yun‐Feng; Wang, Chia–Nan; Dang, Hoang-Sa; Lai, Shun-Te

doi:10.3390/su70810664

Cited by 14 publications

(12 citation statements)

References 27 publications

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“…This algorithm uses transition matrices to divide the state and to calculate the probability, so the future evolution of the conditional probability depends on the current state of the system, but not on its history. For this reason, the Markov chain plays a remedial role in overcoming the limitation of the grey model and has become an interesting component in improvements to the capability of the grey forecasting model in recent works [9,14,[16][17][18]. This literature indicates that the Markov chain has significantly increased the accuracy of the grey forecasting model.…”

Section: Markov Direct Grey Model (The Mdgm(21) Model)mentioning

confidence: 99%

Evaluating Performance of the DGM(2,1) Model and Its Modified Models

et al. 2016

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Abstract:The direct grey model (DGM(2,1)) is considered for fluctuation characteristics of the sampling data in Grey system theory. However, its applications are quite uncommon in the past literature. The improvement of the precision of the DGM(2,1) is only presented in few previous researches. Moreover, the evaluation of forecasted performance of the DGM(2,1) model and its applications was not conducted in previous studies. As the results, this study aims to evaluate forecasted performance of the DGM(2,1) and its three modified models, including the Markov direct grey model MDGM(2,1), the Fourier direct grey model FDGM(2,1), and the Fourier Markov direct grey model FMDGM(2,1) in order to determine the application of the DGM(2,1) model in practical applications and academic research. The results demonstrate that the DGM(2,1) model has lower precision than its modified models, while the forecasted precision of the FDGM(2,1) is better than that of MDGM(2,1). Additionally, the FMDGM(2,1) model presents the best performance among all of the modified models of DGM(2,1), which can effectively overcome the fluctuating of the data sample and minimize the predicted error of the DGM(2,1) model. The finding indicated that the FMDGM(2,1) model does not only have advantages with regard to the sample size requirement, but can also be flexibly applied to the large fluctuation and random sequences with a high quality of estimation.

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Section: Markov Direct Grey Model (The Mdgm(21) Model)mentioning

confidence: 99%

Evaluating Performance of the DGM(2,1) Model and Its Modified Models

et al. 2016

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“…Compared with traditional statistical forecast models, the historical data requirement for model construction of grey predictions can be minimized to at least four data points, the mathematical background for modeling is quite simple, and the forecast performance has high accuracy. For these reasons, grey prediction models have been widely utilized in the different fields of business forecasting, in both previous and current studies [23,[30][31][32][33]. Therefore, grey predictions have become a favorite short-term forecasting technique for understanding the statements with limited past information or difficult to obtain data in the recent three decades.…”

Section: The Grey Forecastingmentioning

confidence: 99%

“…systems have been found in the literature. Lee et al [35] [31] has proved better than the Bass model and was suggested as an effective method to forecast the level of competition. For the other case, an L.V.…”

Section: The Lotka-vottera Systemmentioning

confidence: 99%

An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry

et al. 2016

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Abstract:In real practice, forecasting under the limited data has attracted more attention in business activities, especially in the healthcare traveling industry in its current stage. However, there are only a few research studies focusing on this issue. Thus, the purposes of this paper were to determine the forecasted performance of several current forecasting methods as well as to examine their applications. Taking advantage of the small data requirement for model construction, three models including the exponential smoothing model, the Grey model GM(1,1), and the modified Lotka-Volterra model (L.V.), were used to conduct forecasting analyses based on the data of foreign patients from 2001 to 2013 in six destinations. The results indicated that the L.V. model had higher prediction power than the other two models, and it obtained the best forecasting performance with an 89.7% precision rate. In conclusion, the L.V. model is the best model for estimating the market size of the healthcare traveling industry, followed by the GM(1,1) model. The contribution of this study is to offer a useful statistical tool for short-term planning, which can be applied to the healthcare traveling industry in particular, and for other business forecasting under the conditions of limited data in general.

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“…Wu and Wang [32] used grey system theory in quality function deployment to analyze customers' dynamic and future requirements instead of using static and present customer needs. Huang et al [33] employed a grey model to forecast the trends of Taiwan's electronic paper industry.…”

Section: Proposed Grey Gm (11) Modelmentioning

confidence: 99%

An Integrated Approach to Evaluating and Selecting Green Logistics Providers for Sustainable Development

Wang

Luo

et al. 2017

Sustainability

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Balancing economic development with environmental protection has become a critical concern worldwide. However, along with the highly competitively global marketplace, economic factors are known to directly affect an enterprise's development and its future business. Therefore, selecting the right partner for sustainable collaboration that will lead to improved business performance and reduce carbon dioxide (CO 2 ) emissions is a significant problem for many enterprises. In addition, investigating the economic impact of companies that are charged to protect the environment is becoming increasingly problematic. Thus, the purpose of this paper is to evaluate the comparative efficiencies of 16 Green Logistics Providers (GLPs) in the USA from 2012 to 2015, and the projected four-year period of 2016-2019, by means of an integrated approach that combines the grey forecasting model GM (1,1) and Data Envelopment Analysis (DEA). The results show that there are two GLPs, Knight Transportation and the Union Pacific Corporation, that possess a higher efficiency level and are achieving positive technical change. However, this study also determined that Hyster-Yale Materials Handling and CSX Corporation did not reach an acceptable efficiency score; therefore, they should improve technical efficiency to mitigate environmental concerns. This completely integrative methodology has the potential to provide the best decision-making strategies for finding suitable collaborative partners who are able to meet the sustainability requirements in most economic and environmental areas.

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Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model

Cited by 14 publications

References 27 publications

Evaluating Performance of the DGM(2,1) Model and Its Modified Models

Evaluating Performance of the DGM(2,1) Model and Its Modified Models

An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry

An Integrated Approach to Evaluating and Selecting Green Logistics Providers for Sustainable Development

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