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.

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.

A novel fractional discrete grey model with an adaptive structure and its application in electricity consumption prediction

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yitong Liu ◽  
Yang Yang ◽  
Dingyu Xue ◽  
Feng Pan
Keyword(s):  
Fractional Order ◽  
Electricity Consumption ◽  
Grey Model ◽  
The Novel ◽  
Raw Data ◽  
Content Type ◽  
Power Load ◽  
Grey Models ◽  
Novel Model ◽  
Consumption Prediction

PurposeElectricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.Design/methodology/approachThe novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.FindingsTwo cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.Originality/valueA fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.

Design and vibration analysis of a self-adaptive piston in a novel nanoimprint system

2017 ◽  
Vol 69 (4) ◽  
pp. 591-597
Author(s):  
Chaoran Liu ◽  
Yufeng Su ◽  
Jinzhao Yue ◽  
Junjie Wang ◽  
Weiwei Xia ◽  
...  
Keyword(s):  
Nanoimprint Lithography ◽  
Vibration Analysis ◽  
Design Methodology ◽  
The Self ◽  
Ball Screw ◽  
Piston Rings ◽  
Content Type ◽  
Simulation Results ◽  
The One ◽  
Self Adaptive

Purpose A self-adaptive piston is designed for the compressional gas cushion press nanoimprint lithography system. It avoids the lube pollution and high wear of traditional piston. Design/methodology/approach The self-adaptive piston device consists of symmetrical piston bodies, piston rings and other parts. The two piston bodies are linked by a ball-screw. The locking nut adjusts the distance between two piston bodies to avoid the piston rings from being stuck. The piston rings are placed between two piston bodies. Findings The simulation results based on COMSOL indicate that cylinder vibration caused by self-adaptive piston is 15.9 times smaller than the one caused by a traditional piston. Originality/value The self-adaptive piston is superior to the traditional piston in decreasing cylinder vibration.

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.

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.

scholarly journals On Novel Nonhomogeneous Multivariable Grey Forecasting Model NHMGM

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Haixia Wang ◽  
Peiguang Wang ◽  
M. Tamer Şenel ◽  
Tongxing Li
Keyword(s):  
Least Square Method ◽  
Least Square ◽  
Forecasting Model ◽  
Model Parameters ◽  
The Novel ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Nonlinear Relation ◽  
Novel Model

A novel nonhomogeneous multivariable grey forecasting model termed NHMGM(1,m,kp,c) is proposed in this paper for use in nonhomogeneous multivariable exponential data sequences. The NHMGM(1,m,kp,c) model is able to reflect the nonlinear relation of the data sequences in the system, and it is proved that many classic grey forecasting models can be derived from NHMGM(1,m,kp,c) model. Parameters of the novel model are obtained by using least square method, and the time response function is given. A numerical example is presented to show the effectiveness of the proposed model, six different grey forecasting models are built for modeling, and two popular accuracy criteria (ARPE and MAPE) are adopted to test the reliability of the novel model. The example demonstrates that NHMGM-2 model provides favorable performance compared with the other five grey models. Additionally, the multiplication transformation properties of NHMGM(1,m,kp,c) are systematically analysed, which establish a theoretical foundation for further applications of the model.

A self-adaptive alignment strategy for large components based on dynamic compliance center

2019 ◽  
Vol 39 (2) ◽  
pp. 345-355 ◽  
Author(s):  
Fuzhou Du ◽  
Ke Wen ◽  
Hao Yu
Keyword(s):  
Dynamic Compliance ◽  
The Self ◽  
Large Component ◽  
Content Type ◽  
Physical Constraints ◽  
Geometric Precision ◽  
Coordinate Transfer ◽  
Alignment Process ◽  
Alignment Strategy ◽  
Self Adaptive

PurposeAiming at the problems of geometric precision misalignment and unconsidered physical constraints between large components during the measurement-assisted assembly, a self-adaptive alignment strategy based on the dynamic compliance center (DCC) is proposed in this paper, using force information to guide alignment compliantly.Design/methodology/approachFirst, the self-adaptive alignment process of large components is described, and its geometrical and mechanical characteristics are analyzed based on six-dimensional force/torque (F/T). The setting method of DCC is studied and the areas of DCC are given. Second, the self-adaptive alignment platform of large components driven by the measured six-dimensional F/T is constructed. Based on this platform, the key supporting technologies, including principle of self-adaptive alignment, coordinate transfer, calculation of six-dimensional F/T and alignment process control, are illustrated.FindingsUsing the presented strategy, the position and orientation of large component is adjusted adaptively responding to measured six-dimensional F/T and the changes of contact states are consistent with the strategy. Through the setting of DCC, alignment process runs smoothly without jamming.Practical implicationsThis strategy is applied to the alignment experiment of large components muff coupling. The experimental results show that the proposed alignment strategy is correct and effective and meets the real-time requirement.Originality/valueThis paper proposed a novel way to apply force information in large component self-adaptive alignment, and the setting method of DCC was presented to make the alignment process more feasible.

scholarly journals Explanation of terms of grey forecasting models

2017 ◽  
Vol 7 (1) ◽  
pp. 123-128 ◽  
Author(s):  
Sifeng Liu ◽  
Yingjie Yang
Keyword(s):  
System Theory ◽  
Design Methodology ◽  
Grey System Theory ◽  
Grey System ◽  
Content Type ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Key Terms ◽  
Practical Implications

Purpose The purpose of this paper is to present the terms of grey forecasting models and techniques. Design/methodology/approach The definitions of basic terms about grey forecasting models and techniques are presented one by one. Findings The reader could know the basic explanation about the important terms about various grey forecasting models and techniques from this paper. Practical implications Many of the authors’ colleagues thought that unified definitions of key terms would be beneficial for both the readers and the authors. Originality/value It is a fundamental work to standardise all the definitions of terms for a new discipline. It is also propitious to spread and universal of grey system theory.

Persuasion: Fellow Creatures

2017 ◽  
Author(s):  
Pam Morris
Keyword(s):  
The Self ◽  
The Novel ◽  
Class Relations ◽  
Narrative Perspective ◽  
Class Hierarchies ◽  
A Chain ◽  
Porous Boundaries

Persuasion overtly foregrounds the self as embodied: physical accidents and sickness are recurrent. Sir Walter Eliot’s belief in the time-defying bodily grace of nobility is subject to Austen’s harshest irony. The transition from vertically ordered place to horizontal space in Persuasion is more extreme than in any other of the completed novels. Anne Elliot’s movement from social exclusiveness to socially inclusive possibility allows Austen to challenge gender and class hierarchies traditionally held to be inborn. Her writerly experimentation expands the possibilities of narrative perspective to encompass the porous boundaries of the physical, the emotional and the rational that constitute any moment of consciousness. Her focalisation techniques in the text look directly towards Woolf’s stylist innovations. A chain of references to guns and shooting gathers into the novel contentious contemporary discursive networks on class relations, notions of masculinity and the nature of creaturely life.

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