The effect of sample size on the grey system model

Wu, Lifeng; Liu, Sifeng; Yao, Ligen; Yan, Shuli

doi:10.1016/j.apm.2013.01.018

Cited by 118 publications

(78 citation statements)

References 22 publications

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“…We consider an example from paper [30] which provides the sample data. We will build four models that Error inception MAPE = 1 n…”

Section: Verification Of 푝/푞 Modelmentioning

confidence: 99%

“…Case 2 (logistics demand forecasting in Jiangsu province [30]). We consider an example from paper [30] which provides the sample data.…”

Section: Verification Of 푝/푞 Modelmentioning

confidence: 99%

“…The NDGM 푝/푞 model is more stable than NDGM model. Case 1 (syphilis incidence predicted in China [30]). The trends of syphilis incidence in China forecasting example [31] compare the precision; the historical incidence of China from 2000 to 2008 was employed as the model-fitting.…”

Section: Theorem 10 For the Ndgm 푝/푞 Model Of Original Datamentioning

confidence: 99%

See 2 more Smart Citations

Research on the Stability of NDGM Model with the Fractional Order Accumulation and Its Optimization

Duan

Shao

Xiao

et al. 2017

Mathematical Problems in Engineering

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The grey forecasting model has been successfully applied in numerous fields since it was proposed. The nonhomogeneous discrete grey model (NDGM) was approximately constructed based on the nonhomogeneous index trend; it increased the applicability of discrete grey model. However, the NDGM required accurate data and better effect when the original data did not meet the conditions and fitting and prediction errors were larger. For this, the NDGM with the fractional order accumulating operator (abbreviated as NDGM 푝/푞 ) has higher performance. In this paper, the matrix perturbation bound of the parameters was used to analyze the stability of NDGM 푝/푞 and the NDGM 푝/푞 can decrease the disturbance bound. Subsequently, the parameter estimation method of NDGM 푝/푞 was studied and the Particle Swarm Optimization algorithm was employed to optimize the order number of NDGM 푝/푞 and some steps were provided. In addition, the results of two practical examples demonstrated that the perturbation of NDGM 푝/푞 was smaller than that of NDGM and provided remarkable predication performance compared with the traditional NDGM model and DGM model.

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“…We consider an example from paper [30] which provides the sample data. We will build four models that Error inception MAPE = 1 n…”

Section: Verification Of 푝/푞 Modelmentioning

confidence: 99%

“…Case 2 (logistics demand forecasting in Jiangsu province [30]). We consider an example from paper [30] which provides the sample data.…”

Section: Verification Of 푝/푞 Modelmentioning

confidence: 99%

See 1 more Smart Citation

Research on the Stability of NDGM Model with the Fractional Order Accumulation and Its Optimization

Duan

Shao

Xiao

et al. 2017

Mathematical Problems in Engineering

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show abstract

“…The literature [46] shows that when x (0) (t) is disturbed, the disturbance boundary L(x (0) (t)) of the parameter is an increasing function of n. Thus, when n increases, the parameter perturbation boundary increases, and the grey system model requires small sample modeling [46]. The rolling prediction model has better adaptability in practice and has been successfully applied in the fields of energy, electricity and financial forecasting [37,42,43,47].…”

Section: Rolling Seasonal Grey Model Of Ctago Sequences: Rsdgm(11)mentioning

confidence: 99%

“…However, the DGM(1,1) model of the CTAGO sequence is still a small sample data model, which needs to be improved in the longer sequence. Wu [44][45][46] used matrix perturbation theory to explain the small sample data size of the grey prediction model.…”

Section: Rolling Seasonal Grey Model Of Ctago Sequences: Rsdgm(11)mentioning

confidence: 99%

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Yang

Xiao

Mao

et al. 2016

Entropy

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This paper studies the grey coupled prediction problem of traffic data with panel data characteristics. Traffic flow data collected continuously at the same site typically has panel data characteristics. The longitudinal data (daily flow) is time-series data, which show an obvious intra-day trend and can be predicted using the autoregressive integrated moving average (ARIMA) model. The cross-sectional data is composed of observations at the same time intervals on different days and shows weekly seasonality and limited data characteristics; this data can be predicted using the rolling seasonal grey model (RSDGM (1,1)). The length of the rolling sequence is determined using matrix perturbation analysis. Then, a coupled model is established based on the ARIMA and RSDGM(1,1) models; the coupled prediction is achieved at the intersection of the time-series data and cross-sectional data, and the weights are determined using grey relational analysis. Finally, numerical experiments on 16 groups of cross-sectional data show that the RSDGM(1,1) model has good adaptability and stability and can effectively predict changes in traffic flow. The performance of the coupled model is also better than that of the benchmark model, the coupled model with equal weights and the Bayesian combination model.

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Forecasting Air Quality Indicators for 33 Cities in China

Zhao

2019

CLEAN Soil Air Water

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The Jing‐Jin‐Ji (Beijing‐Tianjin‐Hebei) and the surrounding Jin‐Lu‐Yu (Shanxi‐Shandong‐Henan) regions are experiencing severe problems with air pollution. To measure the change in pollutant concentrations over the next few years, the fractional order accumulation grey model is used to forecast the air quality indicators. The results show that the concentrations of fine particulate matter, particulate matter 10, and sulfur dioxide show a downward trend, and 8‐h O3 (annual) and 8‐h O3 (spring and summer) show an upward trend in these areas. There is a slight increase in nitrogen dioxide concentration in Shanxi, but its concentration in other areas shows a slight decline. The results obtained can provide a clear direction for air quality management decisions.

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The effect of sample size on the grey system model

Cited by 118 publications

References 22 publications

Research on the Stability of NDGM Model with the Fractional Order Accumulation and Its Optimization

Research on the Stability of NDGM Model with the Fractional Order Accumulation and Its Optimization

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Forecasting Air Quality Indicators for 33 Cities in China

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