RETRACTED ARTICLE: Application of ARIMA and SVM mixed model in agricultural management under the background of intellectual agriculture

Wen, Qing; Wang, Yapeng; Zhang, Haodong; Li, Zhen

doi:10.1007/s10586-018-2298-5

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…Support vector machine (SVM) algorithm is a machine learning method, which is more suitable for the problems of small sample, nonlinearity, over-tting and dimension disaster when compared with others, emphasizing simultaneously on minimizing empirical and expected risks. [21][22][23] The main idea of SVM is to map input space to highdimensional feature space using kernel function, and obtains the non-linear relationship between input and output variables. The generalization ability of the model can be improved by minimizing the structure risk, and obtain good statistical results in the case of fewer input samples.…”

Section: Introductionmentioning

confidence: 99%

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Pan

Zhou

et al. 2020

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Pan

Zhou

et al. 2020

RSC Adv.

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“…In recent years, several forecasting models have been integrated with the ARIMA model in the agriculture sector using time series data to improve forecasting results (Mahto et al, 2019;Iqbal et al, 2005;Wang et al, 2018;Wen et al, 2019). Compared to other forecasting approaches like simulation and intelligence techniques, ARMA-GARCH models offer the advantages of accuracy and explicitness, as well as the capacity to accommodate heteroskedasticity.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis and Forecasting of Rice Production Behavior in Bangladesh

Parvin

2022

IUBAT Rev.

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This research looks at how numerous factors, including the price of rice, area, improved seed distribution, areas irrigated by different improved methods, and fertilizer, influence rice production in Bangladesh. This study employed the autoregressive distributed lag (ARDL) model to investigate the short-run and long-run effects of each exogenous variable on rice production. In this research work, auto regressive integrated moving average (ARIMA) and hybrid ARIMA-(general autoregressive conditional heteroskedastic) GRACH were used to forecast rice production in Bangladesh from 1983 to 2030. The parameters of price of rice, area irrigated by different improved methods, area, and fertilizer were found to have a significant and positive impact on rice production in this study, both in the short run and the long run, according to the ARDL model. According to the lowest Root Mean Squared Error, Mean Absolute Error and Theil Inequality Coefficient criteria, the linear hybrid ARIMA (11, 1, 11)–EGARCH (1, 1)–M model performed better in predicting the rice production in Bangladesh. IUBAT Review—A Multidisciplinary Academic Journal, 5 (1): 1-14

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“…For the purposeful development of agriculture, effective planning and forecasting of its state in the future are necessary [6][7][8]. The adoption of an optimal management decision should be based on the adaptation of agricultural production to changing natural, economic and other factors of influence, which is determined by a realistic scientific assessment of development trends in the short, medium and long term.…”

Section: Introductionmentioning

confidence: 99%

Persistent analysis and evaluation of the efficiency of manageability of agricultural organizations in the region

Белокопытов

Mirolyubova²

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The article analyzes the sustainability of the development of agricultural production in modern conditions. On the basis of mathematical modeling, the effectiveness of managing production processes in agricultural organizations in the regions of the Central Federal District of the Russian Federation is studied. As part of the study, a methodology for the integral assessment of the effectiveness of agricultural production management using the Hurst index and the fractal dimension index was developed, while correlations between production volumes and the efficiency of resource use were revealed. The developed proposals make it possible to evaluate the effectiveness of agricultural production management, taking into account a synthetic indicator that takes into account the dynamic properties of production and economic processes in agriculture, qualitative changes in factors and development conditions.

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RETRACTED ARTICLE: Application of ARIMA and SVM mixed model in agricultural management under the background of intellectual agriculture

Cited by 11 publications

References 22 publications

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Analysis and Forecasting of Rice Production Behavior in Bangladesh

Persistent analysis and evaluation of the efficiency of manageability of agricultural organizations in the region

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