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.

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.

Condition monitoring based on modified CUSUM and EWMA control charts

2018 ◽  
Vol 24 (1) ◽  
pp. 119-132 ◽  
Author(s):  
Suzana Paula Gomes Fernando da Silva Lampreia ◽  
José Fernando Gomes Requeijo ◽  
José António Mendonça Dias ◽  
Valter Martins Vairinhos ◽  
Patrícia Isabel Soares Barbosa
Keyword(s):  
Control Charts ◽  
Moving Average ◽  
Failure Detection ◽  
Parameters Estimation ◽  
False Alarms ◽  
Model Parameters ◽  
Content Type ◽  
Catastrophic Failures ◽  
The Right ◽  
Weighted Moving Average

Purpose The application of condition-based maintenance on selected equipment can allow online monitoring using fixed, half-fixed or portable sensors. The collected data not always allow a straightforward interpretation and many false alarms can happen. The paper aims to discuss these issues. Design/methodology/approach Statistical techniques can be used to perform early failure detection. With the application of Cumulative Sum (CUSUM) Modified Charts and the Exponentially Weighted Moving Average (EWMA) Charts, special causes of variation can be detected online and during the equipment functioning. Before applying these methods, it is important to check data for independence. When the independence condition is not verified, data should be modeled with an ARIMA (p, d, q) model. Parameters estimation is obtained using the Shewhart Traditional Charts. Findings With data monitoring and statistical methods, it is possible to detect any system or equipment failure trend, so that we can act at the right time to avoid catastrophic failures. Originality/value In this work, an electro pump condition is monitored. Through this process, an anomaly and four stages of aggravation are forced, and the CUSUM and EWMA modified control charts are applied to test an online equipment monitoring. When the detection occurs, the methodology will have rules to define the degree of intervention.

A new grey prediction model considering the data gap compensation

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Che-Jung Chang ◽  
Chien-Chih Chen ◽  
Wen-Li Dai ◽  
Guiping Li
Keyword(s):  
Experimental Results ◽  
Small Data ◽  
Grey Prediction ◽  
Data Set ◽  
Forecasting Accuracy ◽  
Content Type ◽  
Background Values ◽  
Managerial Decisions ◽  
Modeling Process ◽  
Grey Models

PurposeThe purpose of this paper is to develop a small data set forecasting method to improve the effectiveness when making managerial decisions.Design/methodology/approachIn the grey modeling process, appropriate background values are one of the key factors in determining forecasting accuracy. In this paper, grey compensation terms are developed to make more appropriate background values to further improve the forecasting accuracy of grey models.FindingsIn the experiment, three real cases were used to validate the effectiveness of the proposed method. The experimental results show that the proposed method can improve the accuracy of grey predictions. The results further indicate that background values determined by the proposed compensation terms can improve the accuracy of grey model in the three cases.Originality/valuePrevious studies determine appropriate background values within the limitation of traditional grey modeling process, while this study makes new background values without the limitation. The experimental results would encourage researchers to develop more accuracy grey models without the limitation when determining background values.

A new double exponentially weighted moving average control chart using repetitive sampling

2018 ◽  
Vol 35 (2) ◽  
pp. 387-404 ◽  
Author(s):  
Olatunde Adebayo Adeoti
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Mean Shift ◽  
Exponentially Weighted Moving Average ◽  
Process Mean ◽  
Content Type ◽  
Design And Implementation ◽  
Average Control ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Purpose The purpose of this paper is to propose a double exponentially weighted moving average control chart using repetitive sampling (RS-DEWMA) for a normally distributed process variable to improve the efficiency of detecting small process mean shift. Design/methodology/approach The algorithm for the implementation of the proposed chart is developed and the formulae for the in-control and out-of-control average run lengths (ARLs) are derived. Tables of ARLs are presented for various process mean shift. The performance of the proposed chart is investigated in terms of the average run-length for small process mean shift and compared with the existing DEWMA control chart. Numerical examples are given as illustration of the design and implementation of the proposed chart. Findings The proposed control chart is more efficient than the existing DEWMA control chart in the detection of small process mean shifts as it consistently gives smaller ARL values and quickly detects the process shift. However, the performance of the proposed chart relatively deteriorates for large smoothing constants. Practical implications The application of repetitive sampling in the control chart literature is gaining wide acceptability. The design and implementation of the RS-DEWMA control chart offers a new approach in the detection of small process mean shift by process control personnel. Originality/value This paper fills a gap in the literature by examining the performance of the repetitive sampling DEWMA control chart. The use of repetitive sampling technique in the control chart is discussed in the literature, however, its use based on the DEWMA statistic has not been considered in this context.

Progressive mean exponentially weighted moving average control chart for monitoring the process location

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nurudeen Ayobami Ajadi ◽  
Osebekwin Asiribo ◽  
Ganiyu Dawodu
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Performance Measure ◽  
Exponentially Weighted Moving Average ◽  
Average Scheme ◽  
Content Type ◽  
The Mean ◽  
Memory Type ◽  
Weighted Moving Average ◽  
Exponentially Weighted

PurposeThis study aims to focus on proposing a new memory-type chart called progressive mean exponentially weighted moving average (PMEWMA) control chart. This memory-type chart is an improvement on the existing progressive mean control chart, to detect small and moderate shifts in a process.Design/methodology/approachThe PMEWMA control chart is developed by using a cumulative average of the exponentially weighted moving average scheme known as the progressive approach. This scheme is designed based on the assumption that data follow a normal distribution. In addition, the authors investigate the robustness of the proposed chart to the normality assumption.FindingsThe variance and the mean of the scheme are computed, and the mean is found to be an unbiased estimator of the population mean. The proposed chart's performance is compared with the existing charts in the literature by using the average run-length as the performance measure. Application examples from the petroleum and bottling industry are also presented for practical considerations. The comparison shows that the PMEWMA chart is quicker in detecting small shifts in the process than the other memory-type charts covered in this study. The authors also notice that the PMEWMA chart is affected by higher kurtosis and skewness.Originality/valueA new memory-type scheme is developed in this research, which is efficient in detecting small and medium shifts of a process mean.

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.

GM(1,1) Model of Moving Average Operation to the Original Sequence and its Application

2011 ◽  
Vol 225-226 ◽  
pp. 381-384
Author(s):  
Bin Zeng ◽  
Wu Jun Zeng
Keyword(s):  
Data Processing ◽  
Integral Method ◽  
System Development ◽  
Moving Average ◽  
Original Data ◽  
Forecasting Model ◽  
Original Sequence ◽  
Background Values ◽  
Equal Interval ◽  
Grey Forecasting

The grey forecasting model has been successfully adopted in various fields and its accuracy is closely related with the original data. Improving the smoothness of the original sequence can increase the accuracy of GM(1,1) model and many researchers have done such work about the original sequence improvement. The paper adopts moving average operation on the original sequence and gets the new sequence with good smoothness. In the process of the establishment of GM(1,1) model the paper adopts the integral method to get the background values and expand the equal interval into the unequal interval. The examples show the method can fit and predict the system development more accurately which provides a new way for the data processing.

Research and application of novel Euler polynomial-driven grey model for short-term PM10 forecasting

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiwang Xiang ◽  
Xin Ma ◽  
Minda Ma ◽  
Wenqing Wu ◽  
Lang Yu
Keyword(s):  
Real World ◽  
Pm10 Concentration ◽  
Euler Polynomial ◽  
Grey Model ◽  
Grey System ◽  
Short Term ◽  
Content Type ◽  
New Model ◽  
Proposed Model ◽  
Grey Models

PurposePM10 is one of the most dangerous air pollutants which is harmful to the ecological system and human health. Accurate forecasting of PM10 concentration makes it easier for the government to make efficient decisions and policies. However, the PM10 concentration, particularly, the emerging short-term concentration has high uncertainties as it is often impacted by many factors and also time varying. Above all, a new methodology which can overcome such difficulties is needed.Design/methodology/approachThe grey system theory is used to build the short-term PM10 forecasting model. The Euler polynomial is used as a driving term of the proposed grey model, and then the convolutional solution is applied to make the new model computationally feasible. The grey wolf optimizer is used to select the optimal nonlinear parameters of the proposed model.FindingsThe introduction of the Euler polynomial makes the new model more flexible and more general as it can yield several other conventional grey models under certain conditions. The new model presents significantly higher performance, is more accurate and also more stable, than the six existing grey models in three real-world cases and the case of short-term PM10 forecasting in Tianjin China.Practical implicationsWith high performance in the real-world case in Tianjin China, the proposed model appears to have high potential to accurately forecast the PM10 concentration in big cities of China. Therefore, it can be considered as a decision-making support tool in the near future.Originality/valueThis is the first work introducing the Euler polynomial to the grey system models, and a more general formulation of existing grey models is also obtained. The modelling pattern used in this paper can be used as an example for building other similar nonlinear grey models. The practical example of short-term PM10 forecasting in Tianjin China is also presented for the first time.

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.

scholarly journals The performance of control charts with economic-statistical design when parameters are estimated

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahmoud Alsaid ◽  
Rania M. Kamal ◽  
Mahmoud M. Rashwan
Keyword(s):  
Control Chart ◽  
Economic Performance ◽  
Control Charts ◽  
Moving Average ◽  
Statistical Design ◽  
Exponentially Weighted Moving Average ◽  
Expected Cost ◽  
Content Type ◽  
Estimated Parameters ◽  
Weighted Moving Average

Purpose This paper presents economic and economic–statistical designs of the adaptive exponentially weighted moving average (AEWMA) control chart for monitoring the process mean. It also aims to compare the effect of estimated process parameters on the economic performance of three charts, which are Shewhart, exponentially weighted moving average and AEWMA control charts with economic–statistical design. Design/methodology/approach The optimal parameters of the control charts are obtained by applying the Lorenzen and Vance’s (1986) cost function. Comparisons between the economic–statistical and economic designs of the AEWMA control chart in terms of expected cost and statistical measures are performed. Also, comparisons are made between the economic performance of the three competing charts in terms of the average expected cost and standard deviation of expected cost. Findings This paper concludes that taking into account the economic factors and statistical properties in designing the AEWMA control chart leads to a slight increase in cost but in return the improvement in the statistical performance is substantial. In addition, under the estimated parameters case, the comparisons reveal that from the economic point of view the AEWMA chart is the most efficient chart when detecting shifts of different sizes. Originality/value The importance of the study stems from designing the AEWMA chart from both economic and statistical points of view because it has not been tackled before. In addition, this paper contributes to the literature by studying the effect of the estimated parameters on the performance of control charts with economic–statistical design.

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