Modified Grey Model for Estimating Traffic Tunnel Air Quality

Lee, Cheng-Chung; Wan, Terng-Jou; Kuo, Chao‐Yin; Chung, Chung-Yi

doi:10.1007/s10661-006-9539-4

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…A smoother and exponential growth data contributes higher accuracy [27]. Modified grey model is another one which aims at high accuracy [28, 29]. This suggests that modified grey model could be adopted to in hepatitis B prediction.…”

Section: Discussionmentioning

confidence: 99%

Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China

2018

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BackgroundHepatitis B virus (HBV) infection is a major public health threat in China for China has a hepatitis B prevalence of more than one million people in 2017 year. Disease incidence prediction may help hepatitis B prevention and control. This study intends to build and compare 2 forecasting models for hepatitis B incidence in China.MethodsAutoregressive integrated moving average (ARIMA) model and grey model GM(1,1) were adopted to fit the monthly incidence of hepatitis B in China from March 2010 to October 2017. The fitting and forecasting performances of the 2 models were evaluated. The better one was adopted to predict the incidence from November 2017 to March 2018. Database was built by Excel 2016 and statistical analysis was completed using R 3.4.3 software.ResultsDescriptive analysis showed that the incidence of hepatitis B in China has seasonal variation and has shown a downward trend from 2010 to 2017. We selected the ARIMA (3,1,1) (0,1,2)12 model among all the ARIMA models for it has the lowest AIC value. Model expression of GM (1,1) was X(1) (k + 1) = 3386876.7478e0.0249k − 3289206.7428. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA(3,1,1)(0,1,2)12 model were lower than GM(1,1) model on fitting part and forecasting part. According to the forecast results, the incidence may have a slight fluctuation during the following months.ConclusionsThe ARIMA model showed better hepatitis B fitting and forecasting performance than GM(1,1) model. It is a potential decision supportive tool for controlling hepatitis B in China before a predictive hepatitis B outbreak.

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Section: Discussionmentioning

confidence: 99%

Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China

2018

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“…As far as the data issue is concerned, the present study is limited to comparing forecasting efficiencies of the aforementioned four models on monotonic increasing or decreasing data inputs. It has been claimed that the original GM(1, 1) model is inappropriate in solving forecasting problems when data distribution patterns are with jumping point data inputs [35,36]. Hence, another data issue is whether, in view of the possibility that some time-series data patterns are with jumping points, the forecasting accuracies of the three grey prediction models still can demonstrate good forecast precisions.…”

Section: Discussionmentioning

confidence: 99%

On the fit and forecasting performance of grey prediction models for China’s labor formation

Yin

Tang

2013

Mathematical and Computer Modelling

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a b s t r a c tMotivated by the high probability of inconsistent and insufficient information from official statistics in China, this research paper conducted an out-of-sample forecasting competition between grey prediction and straightforward extrapolation for quantity and quality of laborers in China under the assumption that the performance of grey prediction is superior in dealing with uncertain and insufficient data inputs. In line with previous comparative studies on time-series forecasting techniques, the purpose of this study was to verify that the GM(1,1), GM(1,1) rolling and FGM(1,1) models derived from grey system theory would provide forecasts that are at least as accurate as the straightforward extrapolation approach for China's labor variables. The findings revealed that the forecasting efficiency of GM(1,1) and GM(1,1) rolling models were superior to straightforward extrapolation and FGM(1,1). The results can offer valuable insights and provide a basis for further research in model building for short-term estimation under the circumstances of data incompleteness or information inconsistency.

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“…Tang et al [42] combined MGM with BP neural network to construct a tandem Grey Neural Network model for load forecasting of smart grid. Lee et al [43] used MGM model and GM model to evaluate air quality in traffic tunnels. Wang et al [44] applied MGM to forecast future energy consumption in China and India.…”

Section: Review Of Grey Modelmentioning

confidence: 99%

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Han

2019

Energies

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Forecasting energy demand is the basis for sustainable energy development. In recent years, the new discovery of East Africa’s energy has completely reversed the energy shortage, having turned the attention of the world to the East African region. Systematic research on energy forecasting in Africa, particularly in East Africa, is still relatively rare. In view of this, this study uses a variety of methods to comprehensively predict energy consumption in East Africa. Based on the traditional grey model, this study: (1) Integrated the power coefficient and metabolic principles, and then proposed non-linear metabolic grey model (NMGM) forecasting model; (2) Used Auto Regressive Integrated Moving Average Model (ARIMA) for secondary modeling, and then developed a metabolic grey model-Auto Regressive Integrated Moving Average Model (MGM-ARIMA) and non-linear metabolic grey model-Auto Regressive Integrated Moving Average Model (NMGM-ARIMA) combined models. In terms of the prediction interval, the data for 2000–2017 is a fit to the past stage, while the data for 2018–2030 is used for the prediction of the future stage. To measure the effect of the prediction, the study used the average relative error indicator to evaluate the accuracy of different models. The results indicate that: (1) Mean relative errors of NMGM, MGM-ARIMA, and NMGM-ARIMA are 2.9697%, 2.0969%, and 1.4654%, proving that each prediction model is accurate; (2) Compared with the single model, the combined model has higher precision, confirming the superiority and feasibility of model combination. After prediction, the conclusion shows that East Africa’s primary energy consumption will grow by about 4 percent between 2018 and 2030. In addition, the limitation of this study is that only single variable are considered.

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Modified Grey Model for Estimating Traffic Tunnel Air Quality

Cited by 16 publications

References 17 publications

Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China

Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China

On the fit and forecasting performance of grey prediction models for China’s labor formation

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

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