Abstract
First, four models are established to fit the data of the population for the year 2000–2016. The models include GM (1, 1) model, unary linear regression model, index model, and the logistic growth model. Second, simulation is conducted to demonstrate the four models. The results of the data from the statistical yearbook and website show that the fitting effect is good, and with high accuracy, therefore, we use four models to predict the population of 2017–2020. Then, a combined prediction model derived from the four models is constructed, which is more accurate than the single prediction model indicated from the fit results.
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Acknowledgements
The funding supports of this work are by Philosophy and Social Science Foundation of Daqing: Population prediction model of Daqing city and research on strategic issues (No. DSGB2018110). Philosophy and Social Science Foundation Heilongjiang province: Study on random dynamic effect of Heilongjiang population under the comprehensive “two-child” policy (No. 16TJE01).
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Ye, J., Wang, T., Zhang, Q., Yu, H., Yu, X. (2019). Population Prediction Based on the Multi-models and Comparison Research. In: Patnaik, S., Jain, V. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-10-8944-2_33
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DOI: https://doi.org/10.1007/978-981-10-8944-2_33
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