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.

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.

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.

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.

MGM(1,m) model based on interval grey number sequence and its applications

2017 ◽  
Vol 7 (3) ◽  
pp. 310-319 ◽  
Author(s):  
Pingping Xiong ◽  
Yue Zhang ◽  
Bo Zeng ◽  
Tian-Xiang Yao
Keyword(s):  
Prediction Models ◽  
Development Trend ◽  
Upper And Lower Bounds ◽  
Number Sequence ◽  
Content Type ◽  
Model Based ◽  
Grey Forecasting ◽  
Number Sequences ◽  
Grey Number ◽  
Predicted Values

Purpose Aiming at the traditional multivariate grey forecasting model only considers the modelling of real numbers; therefore, the purpose of this paper is to construct an MGM(1, m) model based on the interval grey number sequences according to the grey modelling theory. Design/methodology/approach First, the multivariable grey number sequences are transformed into the kernel and grey radius sequences which are two feature sequences of interval grey number sequences. Then the MGM(1, m) model for kernel sequences and grey radius sequences are established, respectively. Finally, the simulation and prediction of the upper and lower bounds of the interval grey number sequences are realized by the reductive calculation of the predicted values of the kernel and grey radius. Findings The model is applied to the prediction of visibility and relative humidity, the identification factors of the haze. The results show that the model has high accuracy on the simulation and prediction of multivariable grey number sequences, which is reasonable and practical. Originality/value The main contribution of this paper is to propose a method to simulate and forecast the multivariable grey number sequence that is to establish the prediction models for the whitening sequences of multivariable grey number sequences which are kernel and grey radius sequences and extend the possibility boundary of kernel by grey radius. The model can reflect the development trend of multivariable grey number sequence accurately. When the grey information is continuously complemented, the multivariable grey number prediction model is transformed into the traditional MGM(1, m) model. Therefore, the MGM(1, m) model based on interval grey number sequence is the generalisation and expansion of the traditional MGM(1, m) model.

Workopolis or The Pirate Bay: what does Google Trends say about the unemployment rate?

2019 ◽  
Vol 46 (2) ◽  
pp. 422-445 ◽  
Author(s):  
Maryam Dilmaghani
Keyword(s):  
Unemployment Rate ◽  
Job Search ◽  
Prediction Models ◽  
Leisure Activities ◽  
Google Trends ◽  
Content Type ◽  
Forecasting Models ◽  
First Time ◽  
Research And Policy ◽  
Pirate Bay

Purpose The purpose of this paper is to use data mined from Google Trends, in order to predict the unemployment rate prevailing among Canadians between 25 and 44 years of age. Design/methodology/approach Based on a theoretical framework, this study argues that the intensity of online leisure activities is likely to improve the predictive power of unemployment forecasting models. Findings Mining the corresponding data from Google Trends, the analysis indicates that prediction models including variables which reflect online leisure activities outperform those solely based on the intensity of online job search. The paper also outlines the most propitious ways of mining data from Google Trends. The implications for research and policy are discussed. Originality/value This paper, for the first time, augments the forecasting models with data on the intensity of online leisure activities, in order to predict the Canadian unemployment rate.

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.

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.

A Fourier residual modified Nash nonlinear grey Bernoulli model for forecasting the international trade of Chinese high-tech products

2015 ◽  
Vol 5 (2) ◽  
pp. 165-177 ◽  
Author(s):  
Zhengxin Wang ◽  
Lingling Pei
Keyword(s):  
Time Series ◽  
International Trade ◽  
Nonlinear Time Series ◽  
Small Sample ◽  
Parameters Optimization ◽  
Future Research ◽  
High Tech ◽  
Bernoulli Model ◽  
Content Type ◽  
Grey Models

Purpose – Although the Nash nonlinear grey Bernoulli model (NNGBM(1, 1)) is incomparable with respect to its flexibility over traditional grey models, errors are still inevitable in forecasting. The purpose of this paper is to propose a Fourier residual modified Nash nonlinear grey Bernoulli model (FNNGBM(1, 1)) and use it to forecast the nonlinear time series of the international trade of Chinese high-tech products. Design/methodology/approach – A Fourier series is used to modify the forecasting residual of the NNGBM(1, 1) model, so as to improve its forecasting ability. The parameters optimization of FNNGBM(1, 1) is formulated as a combinatorial optimization problem and is solved collectively using the concept of Nash equilibrium. Findings – The simulation and practical application to fluctuation data both prove that FNNGBM(1, 1) could offer a more precise forecast than NNGBM(1, 1) and the Fourier residual GM(1, 1) (FGM(1, 1)). The import/export data of Chinese high-tech products will maintain rapid growth, with corresponding trade balance enlargement; however, there will be a concomitant decrease in the trade specialization coefficient. Research limitations/implications – This study is deliberately general in its scope and outlook: its focus is mainly on the overall condition of Chinese high-tech products trade. Future research is recommended to analyze specific industrial trade sectors and extraneous influencing factors. Originality/value – An effective method is proposed to enhance the accuracy of NNGBM(1, 1) model in forecasting a small sample, nonlinear time series.

The ingredients of a success recipe

2015 ◽  
Vol 4 (2) ◽  
pp. 162-189 ◽  
Author(s):  
Kaveh Moghaddam
Keyword(s):  
Growth Rate ◽  
Qualitative Analysis ◽  
South Asian ◽  
College Education ◽  
Small Sample ◽  
Qualitative Approach ◽  
South Asian Diaspora ◽  
Content Type ◽  
Asian Diaspora ◽  
Study Results

Purpose – The purpose of this paper is to identify the antecedents of successful South Asian opportunity diaspora entrepreneurship. Furthermore, it examines the successful South Asian diaspora opportunity entrepreneurs’ (DOE) interactions with their country of origin and country of residence. Design/methodology/approach – With a qualitative approach, this study employs the NVivo software to examine a set of semistructured interviews of eight South Asian diaspora entrepreneurs. Findings – The qualitative analysis in this study suggests that a South Asian DOE with a college education, previous industry-related experience, prior startup experience, and a tendency to attribute entrepreneurship talent to training rather than birth exhibits a high-entrepreneurial venture growth rate. Furthermore, the qualitative analysis suggests that a south Asian DOE who emphasizes market analysis, accentuates building the right team of employees, and pursues adventurous choice of financing (i.e. bootstrapping or small bank) exhibits a high-entrepreneurial venture growth rate. Research limitations/implications – Due to the small sample and exploratory nature of the study, results may not be generalized. Future research is encouraged to test the robustness of the findings. Practical implications – The findings of this qualitative study offer implications for immigrant individuals who might have interest in starting a new business and wonder what the ingredients of a diaspora entrepreneurship success recipe might be. Originality/value – This study provides an enhanced understanding of diaspora opportunity entrepreneurship. Furthermore, it contributes to the qualitative approach by offering a novel research design to avoid common problems of researcher bias and mono-method bias.

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