Optimizing Social Economy Prediction based on Integration of Time Series Models

Zhang, Shuaiwei; Zhang, Heng; Zhu, Yu; Jian, Sun; Yuan, Dongqing; Li, Hongran; Zhang, Jian; Bo, Liling; Li, Yunling; Sun, Jing

doi:10.1109/yac51587.2020.9337665

Cited by 3 publications

(4 citation statements)

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“…Neural network techniques were used by Timothy et al [12] to predict hotel revenue, increasing the prediction's accuracy. In order to forecast China's fiscal revenue, Zhang et al [13] took into account the impact factors of inflation and used time series methods.…”

Section: Related Workmentioning

confidence: 99%

Evaluation and Forecasting Models for GGDP

Su,

Huang,

Wang

2024

Frontiers in Artificial Intelligence and Applications

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The evaluation and prediction of GGDP has important implications for global development. We derived positive and negative factors affecting climate change through indicator analysis. We then also developed a grey prediction model and a regression analysis model to predict global climate change. The analysis of the results shows that GGDP benefits from a reduction in carbon emissions of 1791.67665767 Mt C02/year. In addition, we used a quadratic non-linear regression model to analyze the environmental impact of the specific use of GGDP. The model proposed in this paper facilitates the evaluation and prediction of GGDP.

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Section: Related Workmentioning

confidence: 99%

Evaluation and Forecasting Models for GGDP

Su,

Huang,

Wang

2024

Frontiers in Artificial Intelligence and Applications

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“…Timothy et al [6] predicted hotel revenue using neural network methods, improving the prediction accuracy. Zhang et al [7] considered the impact factors of inflation and used time series methods to predict China's fiscal revenue.…”

Section: Related Workmentioning

confidence: 99%

A weighted multivariate combination time series forecasting model

Zhu

2023

Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023)

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In order to further improve the accuracy of time series forecasting, this article proposes a combined prediction model based on four traditional prediction models: GM(1,N), BP neural network, partial least squares regression (PLSR), and triple exponential smoothing (TRIX). The combined prediction model is constructed using quadratic nonlinear programming, while the dynamic weighted combined prediction model is constructed using the reciprocal of variance method. Empirical results show that the combined prediction models effectively mitigate the limitations of the four individual models and exhibit good predictive performance. Among them, the dynamic weighted combined prediction model, compared to the fixed weighted combined prediction model, is more suitable for time series data as the weights have time-varying characteristics.

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“…In recent years, many scholars have used different methods to predict the trend of income change, including financial income, tax budget income, and corporate income. Zhang et al [20] used time series method to forecast financial revenue in China. In addition, they considered the influencing factor of inflation in the prediction and then used the consumer price index to eliminate the impact of inflation.…”

Section: Related Workmentioning

confidence: 99%

“…X (1) (k + 1) � 96775.21804e 0.086k − 89381.31804 (k � 0, 1, 2, • • • , 20). (20) Substituting the data of each year into the above equation, the calculation results are accumulated and reduced to obtain the simulation value, residual variation, and relative variation of the original sample, as shown in Table 1.…”

Section: Example Analysismentioning

confidence: 99%

Prediction of Income of Rural Residents Based on Markov Model under the Background of Rural Revitalization

Xing

2021

Journal of Mathematics

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The income of rural residents is not only an important indicator to measure the development of rural economy, but also a key factor in the livelihood of farmers. The accurate prediction of income of rural residents can provide data supporting for promoting rural revitalization strategy. This paper selects per capita disposable income of rural residents in China as the research object and uses the macrostatistical data from 2011 to 2020 to predict farmers’ income. Firstly, the grey prediction model is constructed, and the grey prediction value is corrected by Markov chain. The simulation value is compared with the real value. And the results show that the prediction accuracy of the model is higher. It shows that the results of using the grey Markov model to predict the income of rural residents in China from 2021 to 2025 are reliable. Finally, the article puts forward policy recommendations to promote the income of rural residents.

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Optimizing Social Economy Prediction based on Integration of Time Series Models

Cited by 3 publications

References 0 publications

Evaluation and Forecasting Models for GGDP

Evaluation and Forecasting Models for GGDP

A weighted multivariate combination time series forecasting model

Prediction of Income of Rural Residents Based on Markov Model under the Background of Rural Revitalization

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