Huizhou GDP forecast based on fractional opposite-direction accumulating nonlinear grey bernoulli markov model-Reference-Cited by-同舟云学术

Huizhou GDP forecast based on fractional opposite-direction accumulating nonlinear grey bernoulli markov model

Published:2023 Issue:2 Volume:31 Page:947-960
ISSN:2688-1594
Container-title:Electronic Research Archive
language:
Short-container-title:era

Author:

Qiu Meilan¹²,Li Dewang²,Luo Zhongliang³,Yu Xijun⁴

Affiliation:

1. Division of Applied and Computational Mathematics, Beijing Computational Science Research Center, Beijing 100193, China

2. School of Mathematics and Statistics, Huizhou University, Guangdong, Huizhou 516007, China

3. School of Electronic and Information Engineering, Huizhou University, Guangdong, Huizhou 516007, China

4. Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing 100088, China

Abstract

<abstract><p>In this paper, a fractional opposite-direction accumulating nonlinear grey Bernoulli Markov model (FOANGBMKM) is established to forecast the annual GDP of Huizhou city from 2017 to 2021. The optimal fractional order number and nonlinear parameters of the model are determined by particle swarm optimization (PSO) algorithm. An experiment is provided to validate the high fitting accuracy of this model, and the effect of prediction is better than that of the other four competitive models such as autoregressive integrated moving average model (ARIMA), grey model (GM (1, 1)), fractional accumulating nonlinear grey Bernoulli model (FANGBM (1, 1)) and fractional opposite-direction accumulating nonlinear grey Bernoulli model (FOANGBM (1, 1)), which proves the robustness of the opposite-direction accumulating nonlinear Bernoulli Markov model. This research will provide a scientific basis and technical references for the economic planning industries.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

General Mathematics

Reference40 articles.

1. Y. C. Yao, Analysis of Economic Factors affecting GDP Growth, Master's Thesis, Harbin Institute of Technology in Harbin, 2014.

2. X. Z. Hao, S. Y. Li, Modeling and forecasting of GDP time series in China, Stat. Decis., 23 (2007), 4–6.

3. T. Liu, W. M. Yang, R. T. Hu, An empirical study on quarterly GDP forecasting of mixed frequency data based on AIC criterion, Stat. Theory Pract., 6 (2021), 26–33. https://doi.org/10.13999/j.cnki.tjllysj.2021.06.006

4. W. G. Wang, Y. Yu, Short-term prediction of quaeterly GDP in China based on MIDAS regression model, J. Quant. Technol. Econ., 33 (2016), 108–125. https://doi.org/10.13653/j.cnki.jqte.2016.04.008

5. M. Zhang, Y. G. Dang, The application of combined forecast model base on wavelets on the predict of Nanjing's GDP, Math. Pract. Theory, 48 (2018), 111–118.

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Huizhou resident population, Guangdong resident population and elderly population forecast based on the NAR neural network Markov model;AIMS Mathematics;2024