Demand prediction in health sector using fuzzy grey forecasting

2018 ◽  
Vol 31 (6) ◽  
pp. 937-949 ◽  
Author(s):  
Ceyda Zor ◽  
Ferhan Çebi
Keyword(s):  
Relevant Literature ◽  
Health Sector ◽  
Healthcare Sector ◽  
Content Type ◽  
Demand Prediction ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Forecasting Performance ◽  
Grey Models ◽  
Health Demand

Purpose The purpose of this paper is to apply GM (1, 1) and TFGM (1, 1) models on the healthcare sector, which is a new area, and to show TFGM (1, 1) forecasting accuracy on this sector. Design/methodology/approach GM (1, 1) and TFGM (1, 1) models are presented. A hospital’s nine months (monthly) demand data is used for forecasting. Models are applied to the data, and the results are evaluated with MAPE, MSE and MAD metrics. The results for GM (1, 1) and TFGM (1, 1) are compared to show the accuracy of forecasting models. The grey models are also compared with Holt–Winters method, which is a traditional forecasting approach and performs well. Findings The results of this study indicate that TFGM (1, 1) has better forecasting performance than GM (1, 1) and Holt–Winters. GM (1, 1) has 8.01 per cent and TFGM (1, 1) 7.64 per cent MAPE, which means excellent forecasting power. So, TFGM (1, 1) is also an applicable forecasting method for the healthcare sector. Research limitations/implications Future studies may focus on developed grey models for health sector demand. To perform better results, parameter optimisation may be integrated to GM (1, 1) and TFGM (1, 1). The demand may be predicted not only for the total demand on hospital, but also for the demand of hospital departments. Originality/value This study contributes to relevant literature by proposing fuzzy grey forecasting, which is used to predict the health demand. Therefore, the new application area as the health sector is handled with the grey model.

Forecasting cocoa production of six major producers through ARIMA and grey models

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tawiah Kwatekwei Quartey-Papafio ◽  
Saad Ahmed Javed ◽  
Sifeng Liu
Keyword(s):  
Growth Rate ◽  
Prediction Models ◽  
Small Sample ◽  
Cocoa Bean ◽  
Production Forecast ◽  
Content Type ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Grey Models ◽  
Cocoa Production

PurposeIn the current study, two grey prediction models, Even GM (1, 1) and Non-homogeneous discrete grey model (NDGM), and ARIMA models are deployed to forecast cocoa bean production of the six major cocoa-producing countries. Furthermore, relying on Relative Growth Rate (RGR) and Doubling Time (Dt), production growth is analyzed.Design/methodology/approachThe secondary data were extracted from the United Nations Food and Agricultural Organization (FAO) database. Grey forecasting models are applied using the data covering 2008 to 2017 as their performance on the small sample size is well-recognized. The models' performance was estimated through MAPE, MAE and RMSE.FindingsResults show the two grey models fell below 10% of MAPE confirming their high accuracy and forecasting performance against that of the ARIMA. Therefore, the suitability of grey models for the cocoa production forecast is established. Findings also revealed that cocoa production in Côte d'Ivoire, Cameroon, Ghana and Brazil is likely to experience a rise with a growth rate of 2.52, 2.49, 2.45 and 2.72% by 2030, respectively. However, Nigeria and Indonesia are likely to experience a decrease with a growth rate of 2.25 and 2.21%, respectively.Practical implicationsFor a sustainable cocoa industry, stakeholders should investigate the decline in production despite the implementation of advanced agricultural mechanization in cocoa farming, which goes further to put food security at risk.Originality/valueThe study presents a pioneering attempt of using grey forecasting models to predict cocoa production.

Forecasting air quality in China using novel self-adaptive seasonal grey forecasting models

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoyue Zhu ◽  
Yaoguo Dang ◽  
Song Ding
Keyword(s):  
Air Quality ◽  
The Self ◽  
Seasonal Adjustment ◽  
The Novel ◽  
Adjustment Factor ◽  
Content Type ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Grey Models ◽  
Self Adaptive

PurposeAiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.Design/methodology/approachThis paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the GM(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal GM(1,1) model (SAGM(1,1)), the self-adaptive seasonal discrete grey model (SADGM(1,1)) and the self-adaptive seasonal fractional-order grey model (SAFGM(1,1)), are put forward for prognosticating the air quality of all provinces in China .FindingsThe experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the SARIMA model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.Research limitations/implicationsSince air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.Practical implicationsGiven the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.Originality/valueThe self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the SARIMA model.

The exponential grey forecasting model for CO2 emissions in Taiwan

2015 ◽  
Vol 5 (3) ◽  
pp. 354-366 ◽  
Author(s):  
Chen-Fang Tsai ◽  
Shin-Li Lu
Keyword(s):  
Moving Average ◽  
Grey Model ◽  
Model Optimization ◽  
Content Type ◽  
Background Values ◽  
Optimal Weights ◽  
Grey Forecasting ◽  
Grey Models ◽  
Forecast Models ◽  
Weighted Moving Average

Purpose – The purpose of this paper is to improve the forecasting efficiency of a grey model. Design/methodology/approach – The exponentially weighted moving average (EWMA) algorithm is proposed to modify background values for a new grey model optimization. Findings – The experimental results reveal that the proposed models (EGM, REGM) outperform traditional grey models. Originality/value – A genetic algorithm (GA) optimizer is used to select the optimal weights for the background values of the EGM(1,1) and REGM(1,1) forecast models. The results of the current study are very encouraging, as the empirical results show that the REGM(1,1) and EGM(1,1) models reduce the MAPE rates over the traditional GM(1,1) and RGM(1,1) models.

Understanding healthcare sector organizations from a dynamic capabilities perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruben Loureiro ◽  
João J. Ferreira ◽  
Jorge Simões
Keyword(s):  
Public Health ◽  
Dynamic Capabilities ◽  
Health Sector ◽  
Healthcare Sector ◽  
Healthcare Organizations ◽  
Health Organizations ◽  
Content Type ◽  
Public Health Organizations ◽  
Management Positions ◽  
The Impact

PurposeDynamic capabilities (DCs) need renewing to respond to changes emerging in the environment, and organizations must build up their capacities to sustain good performance levels. This study aims to identifying and characterizing the DCs existing in public health sector organizations by surveying the DC-related areas in health organizations, contributing to broader and more systematized knowledge in this field.Design/methodology/approachThe authors sent a questionnaire to 245 professionals with leadership and management positions in healthcare organizations in this study. The authors used multivariate methods to validate the variables used to measure the DCs.FindingsIn addition to highlighting the impact of DCs on public health organizations' performance, the study’s results allowed the authors to identify hidden capacities in the organizations of this sector, which only emerge when resource management difficulties occur. These changes and difficulties may interact with users and/or professionals' needs and make organizational management a particular challenge aggravated by quick responses to ensure the organization's survival.Originality/valueThis study contributes to the literature's call for a deeper understanding of the role of DCs and contribute to a greater practical understanding of how these capabilities influence the performance of such organizations.

Using the GM(1,1) model cluster to forecast global oil consumption

2017 ◽  
Vol 7 (2) ◽  
pp. 286-296 ◽  
Author(s):  
Chaoqing Yuan ◽  
Yuxin Zhu ◽  
Ding Chen ◽  
Sifeng Liu ◽  
Zhigeng Fang
Keyword(s):  
Growth Rate ◽  
Model Selection ◽  
Design Methodology ◽  
Oil Consumption ◽  
Medium Term ◽  
Model Cluster ◽  
Content Type ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Term Prediction

Purpose The purpose of this paper is to compare GM(1,1) model, rolling GM(1,1) model and metabolism GM(1,1) model included in the GM(1,1) model cluster and use these models to forecast global oil consumption. Design/methodology/approach Simulated sequences will be generated randomly, and used to test the models included in the GM(1,1) model cluster; and these grey forecasting models are applied to forecast global oil consumption. Findings Effectiveness of these grey forecasting models is proved by random experiments, which explains the model adaptability. Global oil consumption is predicted, and it shows that global oil consumption will increase at a rather big growth rate in the next years. Originality/value The effectiveness of medium-term prediction of these grey forecasting models is analyzed by random experiments. These models are compared, and some basis for model selection is obtained.

Service quality, patient satisfaction and loyalty in the Bangladesh healthcare sector

2017 ◽  
Vol 30 (5) ◽  
pp. 477-488 ◽  
Author(s):  
Selim Ahmed ◽  
Kazi Md. Tarique ◽  
Ishtiaque Arif
Keyword(s):  
Patient Satisfaction ◽  
Service Quality ◽  
Age Groups ◽  
Health Sector ◽  
Young Patients ◽  
Public Hospitals ◽  
Healthcare Sector ◽  
Content Type ◽  
Hospital Patients ◽  
Response Rate Data

Purpose The purpose of this paper is to investigate service quality, patient satisfaction and loyalty in Bangladesh’s healthcare sector. It identifies healthcare quality conformance, patient satisfaction and loyalty based on demographics such as gender, age and marital status. It examines the differences between public and private healthcare sectors regarding service quality, patient satisfaction and loyalty. Design/methodology/approach The authors distributed 450 self-administered questionnaires to hospital patients resulting in 204 useful responses (45.3 per cent response rate). Data were analysed based on reliability analysis, exploratory factor analysis, independent samples t-tests, ANOVA and discriminant analysis using SPSS version 23. Findings Findings indicate that single patients perceive tangibles, reliability, empathy and loyalty higher compared to married patients. Young patients (⩽20 years) have a higher tangibles, empathy and loyalty scores compared to other age groups. The authors observed that private hospital patients perceive healthcare service quality performance higher compared to patients in public hospitals. Research limitations/implications The authors focussed solely on the Bangladesh health sector, so the results might not be applicable to other countries. Originality/value The findings provide guidelines for enhancing service quality, patient satisfaction and loyalty in the Bangladesh healthcare sector and other countries.

The privatisation of healthcare system in Bangladesh

2019 ◽  
Vol 32 (1) ◽  
pp. 97-107 ◽  
Author(s):  
Redwanur Rahman
Keyword(s):  
Public Policy ◽  
Healthcare System ◽  
Health Sector ◽  
World Health ◽  
Healthcare Sector ◽  
Public Healthcare ◽  
Content Type ◽  
The Public ◽  
The World ◽  
The Government

Purpose The purpose of this paper is to examine the factors that triggered the privatisation of Bangladesh’s health sector. Design/methodology/approach This study follows systematic reviews in its undertaking and is based on an extensive review of both published and unpublished documents. Different search engines and databases were used to collect the materials. The study takes into account of various research publications, journal articles, government reports, policy and planning documents, relevant press reports/articles, and reports and discussion papers from the World Health Organization, the World Bank and the Asian Development Bank. Findings While Bangladesh’s healthcare sector has undergone an increasing trend towards privatisation, this move has limited benefits on the overall improvement in the health of the people of Bangladesh. The public sector should remain vital, and the government must remobilise it to provide better provision of healthcare. Research limitations/implications The paper focusses only on the public policy aspect of privatisation in healthcare of a country. Practical implications The paper examines the issue of privatisation of healthcare and concludes that privatisation not only makes services more expensive, but also diminishes equity and accountability in the provision of services. The study, first, makes a spate of observations on improving public healthcare resources, which can be of value to key decision makers and stakeholders in the healthcare sector. It also discourages the move towards private sector interventions. Originality/value This study is an independent explanation of a country’s healthcare system. Lesson learned from this study could also be used for developing public policy in similar socio-economic contexts.

scholarly journals An Improved Nonlinear Grey Bernoulli Model Combined with Fourier Series

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Wang Chia-Nan ◽  
Phan Van-Thanh
Keyword(s):  
Fourier Series ◽  
Data Sets ◽  
Bernoulli Model ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Proposed Model ◽  
Forecasting Performance ◽  
Future Direction ◽  
Residual Errors ◽  
New Procedures

Grey forecasting is a dynamic forecasting model and has been widely used in various fields. In recent years, many scholars have proposed new procedures or new models to improve the precision accuracy of grey forecasting for the fluctuating data sets. However, the prediction accuracy of the grey forecasting models existing may not be always satisfactory in different scenario. For example, the data are highly fluctuating are with lots of noise. In order to deal with this issue, a Fourier Nonlinear Grey Bernoulli Model (1, 1) (abbreviated as F-NGBM (1, 1)) is proposed to enhance the forecasting performance. The proposed model was established by using Fourier series to modify the residual errors of Nonlinear Grey Bernoulli Model (1, 1) (abbreviated as (NGBM (1, 1)). To verify the effectiveness of the proposed model, fluctuation data of the numerical example in Wang et al.’s paper (Wang et al. 2011) and practical application are used. Both of these simulation results demonstrate that the proposed model could forecast more precisely than several different kinds of grey forecasting models. For future direction, this proposed model can be applied to forecast the performance with the high fluctuation data in the different industries.

Improved VaR forecasts using extreme value theory with the Realized GARCH model

2017 ◽  
Vol 34 (2) ◽  
pp. 238-259 ◽  
Author(s):  
Samit Paul ◽  
Prateek Sharma
Keyword(s):  
Extreme Value Theory ◽  
Garch Model ◽  
Value Theory ◽  
Garch Models ◽  
Two Stage ◽  
Data Set ◽  
Content Type ◽  
Forecasting Models ◽  
Stock Indices ◽  
Forecasting Performance

Purpose This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models. Design/methodology/approach The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results. Findings The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR. Originality/value To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.

Predicting product demand from small-sized data: grey models

2017 ◽  
Vol 7 (1) ◽  
pp. 80-96 ◽  
Author(s):  
Asli Özdemir ◽  
Güzin Özdagoglu
Keyword(s):  
Markov Model ◽  
Prediction Models ◽  
Markov Models ◽  
Small Data ◽  
Grey Prediction ◽  
Prediction Problem ◽  
Content Type ◽  
Demand Prediction ◽  
Grey Models ◽  
Prediction Problems

Purpose Prediction problems raised in uncertain environments require different solution approaches such as grey prediction models, which consider uncertainty in information and also enable the use of small data sets. The purpose of this paper is to investigate the comparative performances of grey prediction models (GM) and Markov chain integrated grey models in a demand prediction problem. Design/methodology/approach The modeling process of grey models is initially described, and then an integrated model called the Grey-Markov model is presented for the convenience of applications. The analyses are conducted on a monthly demand prediction problem to demonstrate the modeling accuracies of the GM (1,1), GM (2,1), GM (1,1)-Markov, and GM (2,1)-Markov models. Findings Numerical results reveal that the Grey-Markov model based on GM (2,1) achieves better prediction performance than the other models. Practical implications It is thought that the methodology and the findings of the study will be a significant reference for both academics and executives who struggle with similar demand prediction problems in their fields of interest. Originality/value The novelty of this study comes from the fact that the GM (2,1)-Markov model has been first used for demand prediction. Furthermore, the GM (2,1)-Markov model represents a relatively new approach, and this is the second paper that addresses the GM (2,1)-Markov model in any area.

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