Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm

Ou, Shang-Ling

doi:10.1016/j.compag.2012.03.007

Cited by 94 publications

(45 citation statements)

References 61 publications

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“…To forecast agricultural output in Taiwan, both background value optimization and a genetic algorithm were used to construct a new single-variable grey model. Evaluating the prediction accuracy of a traditional GM(1,1) model and of an improved model, the best performance was achieved by a genetic method, with mean absolute percentage errors (MAPEs) of 2.372% [34]. These studies solved the problem of single-parameter optimization in a grey system, but Choi et al [35] reported that a genetic algorithm can also solve optimization problems with multiple parallel solutions.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

“…According to relevant studies on single-variable grey models optimized by a genetic algorithm [33][34], specific parameters are set as follows (Table 1): Using the genetic algorithm, the optimal parameters of GM(1,N)are selected with the highest fitness values. The optimization process of GA-GM(1,N) is shown in Figure 1.…”

Section: The Modelling Algorithm Of Ga-gm(1n)mentioning

confidence: 99%

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An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

Xie

Zhang

et al. 2018

Energies

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Abstract:In this paper, an improved grey model and scenario analysis, GA-GM(1,N) is proposed to forecast the carbon intensity in the Pearl River Delta (PRD) region, one of the most developed regions in China. Moreover, to show the advantage and feasibility of the proposed model, the forecasting results of the GA-GM(1,N) model are compared with that of a single-variable grey model (GM (1,1)) and a multivariable form (GM(1,N)). Data from one sample period (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) are used to develop the models, and data from another sample period (2013)(2014)(2015) are used to test them. The mean absolute percentage error (MAPE) is applied to measure the accuracy of prediction. The results show that, of the three models, GA-GM(1,N) produces the best carbon intensity forecasts, with MAPEs of 0.4-1.4% and 0.04-0.4% in the development and testing periods respectively. This indicates that the optimization of the genetic algorithm is effective. The realization of carbon reduction targets in different cities is also explored by combining grey models with scenario analysis. Only Guangzhou could achieve its reduction target under all scenarios, and it can serve as a reference for other cities. Policy recommendations are provided based on these results.

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Section: Genetic Algorithmsmentioning

confidence: 99%

Section: The Modelling Algorithm Of Ga-gm(1n)mentioning

confidence: 99%

An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

Xie

Zhang

et al. 2018

Energies

View full text Add to dashboard Cite

show abstract

“…Ahlburg [28] indicates that the MAPE is helpful in comparing various forecasting models. The MAPE has been used widely for measuring forecasting accuracy [8,[29][30][31]; thus, the MAPE is used as the first alternative accuracy measurement to evaluate the performance of the forecasting methods in this paper. The MAPE is defined in Eq.…”

Section: Performance Measuresmentioning

confidence: 99%

The strategy of investment in the stock market using modified support vector regression model

2017

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Abstract. Stock indices forecasting has become a popular research issue in recent years.Although many statistical time series models have been applied in stock indices forecasting, they are limited to certain assumptions. Accordingly, the traditional statistical time series models might not be suitable for forecasting real-life stock indices data. Hence, this paper proposes a novel forecasting model to assist investors in determining a strategy for investments in the stock market. The proposed model is called the modified support vector regression model, which is composed of the correlation coefficient method, the sliding window algorithm and the support vector regression model. The results show that the forecasting accuracy of the proposed model is more stable than the existing models in terms of average and standard deviation of the root mean square error (RMSE) and the mean absolute percentage error (MAPE). Accordingly, the proposed model would be used to assist investors in determining a strategy for investing in stocks.

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“…Some scholars have studied the grey prediction model combined with an algorithm such as the genetic algorithm [36], Verhulst power allocation [37], technique for order of preference by similarity to ideal solution (TOPSIS) [38], and particle swarm optimization (PSO) [39]. These studies dramatically improved the simulative error and prediction accuracy of the grey model.…”

Section: Problems With Predicting Productionmentioning

confidence: 99%

Forecasting China’s Annual Biofuel Production Using an Improved Grey Model

et al. 2015

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Biofuel production in China suffers from many uncertainties due to concerns about the government's support policy and supply of biofuel raw material. Predicting biofuel production is critical to the development of this energy industry. Depending on the biofuel's characteristics, we improve the prediction precision of the conventional prediction method by creating a dynamic fuzzy grey-Markov prediction model. Our model divides random time series decomposition into a change trend sequence and a fluctuation sequence. It comprises two improvements. We overcome the problem of considering the status of future time from a static angle in the traditional grey model by using the grey equal dimension new information and equal dimension increasing models to create a dynamic grey prediction model. To resolve the influence of random fluctuation data and weak anti-interference ability in the Markov chain model, we improve the traditional grey-Markov model with classification of states using the fuzzy set theory. Finally, we use real data to test the dynamic fuzzy prediction model. The results prove that the model can effectively improve the accuracy of forecast data and can be applied to predict biofuel production. However, there are still some defects in our model. The modeling approach used here predicts biofuel production levels based upon past production levels dictated by economics, governmental policies, and technological developments but none of which can be forecast accurately based upon past events.

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Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm

Cited by 94 publications

References 61 publications

An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

The strategy of investment in the stock market using modified support vector regression model

Forecasting China’s Annual Biofuel Production Using an Improved Grey Model

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