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Comparing accuracy of GM(1,1) and grey Verhulst model in Taiwan dental clinics forecasting
Abstract
By applying the GM(1,1) and grey Verhulst model of Grey theory to forecast units, Herfindahl-Hirschman index *HHI), and Gini coefficient of dental clinics in Taiwan, by analyzes the growth trend, geography competition and healthcare distribution, in addition to comparing the accuracy with two models. Measurement results indicate that HHI and Gini coefficient shows the dental clinics tending to proportion in counties, and the average accuracy of the GM(1,1) and grey Verhulst model as applied to growth thends of dental clinics, HHI and Gini coefficient are higher than 99%. Especially, this study identifies the grey Verhulst model creaste higher accuracy in population related issues more than GM(1,1). The model estimates indicate that the number of dental clinics will increase from 2005 to 2007, while the HHI and Gini coefficient will decrease.
... Researchers in Thailand have successfully applied Grey GM(1,1) forecasting model to estimate demand for Thailand's medical tourism product and the related revenues [30]. Other areas where Grey approaches have been put to use include the electronics industry [31] [32] [33], the energy industry [34] [35] [36], the financial sector [37] [38] [39] and the medical field [40] [41], among a host of others. ...
This paper examines the predictive capacity of two Grey Systems Forecasting models. The original Grey GM(1,1) Forecasting model, introduced by Deng[1] [2] together with an improved Grey GM(1,1) model proposed by Ji et al.[3] are used to forecast medical tourism demand for Bermuda. The paper also introduces a quasi-optimization method for the optimization of the alpha (weight) parameter. Five steps ahead out-of-sample forecasts are produced after estimating the models using four data points. The results indicate that the optimization of the alpha parameter substantially improves the predictive accuracy of the models; reducing the five steps ahead out-of-sample Mean Absolute Percentage Error from roughly 7% to roughly 3.80% across the two models. Largely, the forecasting approaches demonstrate significant potential for use as an alternative to the traditional forecasting methods in circum-stances where substantial amounts of high-quality data are not available.
... When = 2, according to (4), (1) is calculated as [31][32][33] (1) ( + 1) = (1) (1) ...
In order to reduce the harm of red tide to marine ecological balance, marine fisheries, aquatic resources, and human health, an optimal Grey Verhulst model is proposed to predict the concentration of nitrogen in seawater, which is the key factor in red tide. The Grey Verhulst model is established according to the existing concentration data series of nitrogen in seawater, which is then optimized based on background value and time response formula to predict the future changes in the nitrogen concentration in seawater. Finally, the accuracy of the model is tested by the posterior test. The results show that the prediction value based on the optimal Grey Verhulst model is in good agreement with the measured nitrogen concentration in seawater, which proves the effectiveness of the optimal Grey Verhulst model in the forecast of red tide.
... The principle of the GM model is that it provides forecasting data for decision-and policy-makers and helps the decision-makers achieve a high level of performance in forecasting the shortterm dataset (Deng, 1982;Deng, 1989). GM(1,1) has been successfully applied to many fields, i.e., tourism demand forecasting (Lin et al., 2009;Huang and Lin, 2011), the prediction of output in the high technology industry (Hsu, 2009;Wang and Hsu, 2008;Lin and Yang, 2003), the prediction of top executive turnover in the electronics industry (Lin et al., 2008), forecasting in relation to dental clinics (Lin et al., 2007), and predicting electric power (Huang et al., 2007). The Grey forecasting model forms the core of Grey system theory, and Grey forecasting always results in highly accurate measurements. ...
The purpose – Accurately forecasting the demand for international health tourism is important to newly-emerging markets in the world. The aim of this study was presents a more suitable and accurate model for forecasting the demand for health tourism that should be more theoretically useful.
Design – Applying GM(1,1) with adaptive levels of α (hereafter GM(1,1)-α model) to provide a concise prediction model that will improve the ability to forecast the demand for health tourism in Asian countries.
Methodology – In order to verify the feasibility of the proposed approach, using available secondary and primary data covering the period from 2002 through 2009 obtained from the RNCOS “Opportunities in Asian Health tourism” report. Based on a unique and characteristics database for the health tourism industry, this study applies the adaptive α in a Grey forecasting model (GM(1,1)-α) to predict the demand for health tourism in Asian countries.
Approach – Implementation of demand forecasting in health tourism is examined on the short-term and limited dataset, due to importance of a minimum the predicated error on underlying basis for the econometric model for health tourism markets.
Findings – Key findings present that the optimal value of α in GM(1,1) can minimize the predicted error. Finally, in the case of the demand for health tourism in Asian countries, using GM(1,1)-α to predict error is clearly better than the use of the original GM(1,1) and time series models.
The originality of this research – The originality comes from the analysis of the demand forecasting in health tourism of Asian countries, which provides an easy and accurate method to predict the demand for health medical tourism and ideas for further improvements in the sector
of health tourism.
For many Caribbean countries, tourism represents a major source of foreign exchange and is the main contributor to gross domestic product. Therefore, the timely production of reliable estimates for tourism demand is paramount, especially in light of its seasonal nature. This article utilises a quasi-optimisation algorithm to produce optimised values for the generation coefficient (weights) of the grey GM(1,1) forecasting model, together with two modified grey GM(1,1) models. With data for 2008–2015, the parameter-optimised models were then used to produce in-sample and out-of-sample forecasts of tourism demand (stay-over tourist arrivals) for selected Caribbean countries. These estimates were also compared to those produced by the standard versions of the models. The results indicate that while the standard models can generate reasonably accurate results, in some instances, the performance of the models improved noticeably with the use of the optimised parameters. Overall, most of the in-sample and out-of-sample mean absolute percentage errors are well below 5%, indicating a high level of forecasting accuracy.
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