Prediction of Agricultural Water Consumption in 2 Regions of China Based on Fractional-Order Cumulative Discrete Grey Model

In this paper, a new forecasting method of agricultural water demand, fractional-order cumulative discrete grey model, is proposed. Firstly, the best fitting of historical data is used to construct the optimization model. MATLAB programming is applied to solve the optimization model and obtain the optimal order. Secondly, the fractional-order cumulative discrete grey model in this paper is compared with GM (1, 1) model to verify the performance of the model. Finally, Handan region of Hebei Province and Jingzhou region of Hubei Province were selected as the study areas to predict their agricultural water consumptions. The results show that the fractional-order cumulative discrete grey model has better prediction performance than the GM (1, 1) model. It can be used as an effective method for forecasting agricultural water consumption.

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A Predictive Analysis of the Business Environment of Economies along the Belt and Road Using the Fractional-Order Grey Model

Journal of Mathematics ◽

10.1155/2021/3153731 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Lingling Pei ◽

Jun Liu

Keyword(s):

Particle Swarm Optimization ◽

Fractional Order ◽

Particle Swarm Optimization Algorithm ◽

Business Environment ◽

Grey Model ◽

Predictive Analysis ◽

Optimal Order ◽

Swarm Optimization ◽

Belt And Road ◽

The Belt And Road

This paper determined the optimal order of FGM (1, 1) model through particle swarm optimization algorithm and combined with the World Bank business environment data to predict and analyze the business environment of economies along the Belt and Road. The empirical results show that the FGM (1, 1) model has a good predicting effect on the business environment. In terms of prediction accuracy, the FGM (1, 1) model based on particle swarm optimization algorithm to determine the optimal order is significantly better than the traditional GM (1, 1) model. The predict results show that the business environment level of economies along the Belt and Road will increase year by year from 2021 to 2022, but the overall level is still relatively low. The main innovation of this paper lies in the introduction of the fractional-order grey model into the predictive analysis of the business environment, which is of great significance to the extension and application of fractional-order models in management and economic systems.

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Forecasting the Cumulative Confirmed Cases with the FGM and Fractional-Order Buffer Operator in Different Stages of COVID-19

Journal of Mathematics ◽

10.1155/2021/6178629 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Yanhui Chen ◽

Minglei Zhang ◽

Kai Lisa Lo ◽

Jackson Jinhong Mi

Keyword(s):

Growth Rate ◽

Empirical Analysis ◽

Fractional Order ◽

Prediction Accuracy ◽

Historical Data ◽

Grey Model ◽

Prevention Measures ◽

Early Stages ◽

The Empirical Analysis ◽

Epidemic Diseases

This study proposes to use the fractional-order accumulation grey model (FGM) combined with the fractional-order buffer operator to predict the cumulative confirmed cases in different stages of COVID-19. In the early stages of COVID-19 outbreak, when the cumulative confirmed cases increased rapidly, we used the strengthening buffer operator in the prediction process. After the government’s prevention measures started to take effect, the growth rate of cumulative confirmed cases slows down. Therefore, the weakening buffer operator is applied in the prediction process. The fractional order of the buffer operator is derived from the historical data, which are more relevant. The empirical analysis of seven countries’ data shows that the FGM with the fractional-order buffer operator achieves the best results for most cases. The fractional-order buffer operator improves the prediction accuracy of the FGM in this study. Our study also suggests a practical way for predicting the trend of epidemic diseases.

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Forecasting CO2 Emissions Using a Novel Conformable Fractional Order Discrete Grey Model

10.21203/rs.3.rs-809289/v1 ◽

2021 ◽

Author(s):

Peng Zhu ◽

Wanli Xie ◽

Yunshen Shi ◽

Mingyong Pang ◽

Yuhui Shi

Keyword(s):

Carbon Dioxide ◽

Fractional Order ◽

Carbon Dioxide Emissions ◽

Industrial Development ◽

Grey Model ◽

Optimal Order ◽

Low Carbon ◽

Grey System ◽

Industrial Carbon ◽

Proposed Model

Abstract Accurate and scientific forecasting of carbon dioxide emissions will help make better industrial carbon emission planning so as to promote low-carbon industrial development and achieve sustainable economic growth. For depressing the disturbance of various elements, grey system-based models play an important role in forecasting science. In this paper, we extend the cumulative order from integer order to fractional order based on the discrete gray model, which we call CFDGM (1,1). After introducing the free quantity of the model order, the accuracy of the prevenient grey-based models can be further enhanced. We selected the data for carbon dioxide production by Germany, Japan, and Thailand for modeling. To obtain the optimal order of our grey model, we selected four optimizers to search for the order. The results show that although the search history of the four types of optimizers is different, the search results are the same, which proves that the four types of optimizers are stable and reliable, and the order for which we searched is reliable. By substituting the optimal order into CFDGM (1,1), we obtained the fitting and prediction error of the proposed model. The final results show that a satisfactory fitting effect and forecasting effect is obtained by our proposed model.

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

Kybernetes ◽

10.1108/k-04-2021-0257 ◽

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.

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APPLING GREY FORECASTING METHOD TO FORECAST THE PORTFOLIO’S RATE OF RETURN IN STOCK MARKET OF IRAN

Australian Journal of Business and Management Research ◽

10.52283/nswrca.ajbmr.20110107a01 ◽

2012 ◽

Vol 01 (07) ◽

pp. 01-16

Author(s):

Ali Mohammadi ◽

Sara Zeinodin Zade

Keyword(s):

Time Series ◽

Stock Market ◽

Predictive Ability ◽

Grey Model ◽

Rate Of Return ◽

Forecasting Accuracy ◽

Grey Forecasting ◽

Best Value ◽

Forecasting Method ◽

Time Series Method

Stock market is one of the most important investment market, which influenced by many factors, therefore it needs a robust and accurate forecasting. In this study ,grey model used as a forecasting method and examined if it is the most reliable forecasting method in comparison of time series method. The information of portfolio’s rate of return is gathered from 50 accepted companies in Tehran stock market, which were announced as the best companies last year. Mean Square of the errors (MSE) is computed by different value of α in grey model which could be varied between .1 to .9 ,to examined if α=.5 is the best value that our model could take .Then the predictive ability of the model is compared with different type of time series based forecasting methods Experimental results confirm forecasting accuracy of grey model. Tracking signal is computed for grey model to see whether grey model forecasting is in control or not. At the last portfolio’s rate of return is forecasted for next periods.

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Does Crop Rotation Enhance Groundwater Health? A Review of the Winter Wheat Fallow Policy in the North China Plain

Water ◽

10.3390/w11112416 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2416

Author(s):

Ming Lei ◽

Yuqian Zhang ◽

Yuxuan Dang ◽

Xiangbin Kong ◽

Jingtao Yao

Keyword(s):

Winter Wheat ◽

Water Consumption ◽

North China Plain ◽

North China ◽

Shallow Groundwater ◽

Agricultural Water ◽

The North ◽

The North China Plain ◽

The World ◽

Shallow Groundwater Level

Agricultural water management is a vital component of realizing the United Nation’s Sustainable Development Goals because of water shortages worldwide leading to a severe threat to ecological environments and global food security. As an agro-intensified irrigation area, the North China Plain (NCP) is the most important grain basket in China, which produces 30%–40% of the maize and 60%–80% of the wheat for China. However, this area has already been one of the largest groundwater funnels in the world due to long-term over-exploitation of groundwater. Due to the low precipitation during the growing period, winter wheat requires a large amount of groundwater to be pumped for irrigation, which consumes 70% of the groundwater irrigation. To alleviate the overexploitation of groundwater, the Chinese government implemented the Winter Wheat Fallow Policy (WWFP) in 2014. The evaluation and summarization of the WWFP will be beneficial for improving the groundwater overexploitation areas under high-intensity irrigation over all the world. So far, there have been few attempts at estimating the effectiveness of this policy. To fill this gap, we assessed the planting area of field crops and calculated the evapotranspiration of crops based on remote-sensed and meteorological data in the key area—Hengshui. We compared the agricultural water consumption before and after the implementation of this policy, and we analyzed the relationship between changes in crop planting structure and groundwater variations based on geographically weighted regression. Our results showed the overall classification accuracies for 2013 and 2015 were 85.56% and 82.22%, respectively. The planting area of winter wheat, as the most reduced crop, decreased from 35.71% (314,053 ha) in 2013 to 32.98% (289,986 ha) in 2015. The actual reduction in area of winter wheat reached 84% of the target (26 thousand ha) of the WWFP. The water consumption of major crops decreased from 2.98 billion m3 of water in 2013 to 2.83 billion m3 in 2015, a total reduction of 146 million m3, and 88.43% of reduced target of the WWFP (166 million m3). The planting changes of winter wheat did not directly affect the change of shallow groundwater level, but ET was positively related to shallow groundwater level and precipitation was negatively related to shallow groundwater levels. This study can provide a basis for the WWFP’s improvement and the development of sustainable agriculture in high-intensity irrigation areas.

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Impact of time zones on forecasting of exchange market based on distribution of expected values

9th International Scientific Conference “Business and Management 2016” ◽

10.3846/bm.2016.29 ◽

2016 ◽

Cited By ~ 1

Author(s):

Nijolė Maknickienė ◽

Algirdas Maknickas

Keyword(s):

New York ◽

Exchange Rates ◽

Historical Data ◽

Trading Strategies ◽

Exchange Market ◽

Time Intervals ◽

Additional Information ◽

Forecasting Method ◽

Time Zones ◽

Expected Values

Forecasting of chaotic changes of exchange rates usually is based on historical data and depends on the choice of time intervals. This study seeks to develop new forecasting method based on data of different time zones. This paper demonstrates how the using of London and New York divisions of the trading day allows getting additional information from predicting exchange rates. This was modelled with the help of ensemble of EVOLINO for obtaining of predictions of the distribution of expected values. The obtained results show that double forecasts evaluation reveals a possible trend in the exchange market and enriches the choice of real-time trading strategies.

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