Efficacy Evaluation of Statistical Prediction Models: A Comparative Analysis based on ARIMA Model, Grey Model and Polynomial Regression Model

Authors

  • Haoru Du

DOI:

https://doi.org/10.54097/hbem.v1i.2316

Keywords:

ARIMA Model; Grey Model; Polynomial Regression Model; Efficacy Evaluation.

Abstract

This paper uses ARIMA model, grey model and polynomial regression model to estimate and forecast two important indicators of China's real GDP growth rate and consumer price index (CPI). The results show that the sequence predicted by the polynomial regression model has the highest degree of agreement with the actual value, and has the lowest prediction error and the best prediction performance, while the other two types of models are not suitable for long-term prediction.

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References

Box G. E. P., Jenkins G. M. Time series analysis: forecasting and control[M]. San Francisco: Holden-Day, 1970.

Deng J. The grey control system[J]. Journal of Huazhong University of Science and Technology, 1982, 10 (3):9-18.

Aslanargun A, Mammadov M, Yazici B, et al. Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting[J]. Journal of Statistical Computation & Simulation, 2007, 77 (1/2):29-53.

Liu S, Xie N, Forrest J. On new models of grey incidence analysis based on visual angle of similarity and nearness [J]. Systems Engineering-Theory & Practice, 2010, 30(5):881-887.

Tao H, Cao W. Principle and Application of Polynomial Regression and Response Surface Analysis[J]. Statistics & Decision, 2020, 36(08):36-40.

Li Z, Liu S. Prediction Comparison Based on ARIMA Model, Grey Model and Regression Model[J]. Statistics & Decision, 2019, 35(23):38-41.

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Published

28-11-2022

How to Cite

Du, H. (2022). Efficacy Evaluation of Statistical Prediction Models: A Comparative Analysis based on ARIMA Model, Grey Model and Polynomial Regression Model. Highlights in Business, Economics and Management, 1, 41-46. https://doi.org/10.54097/hbem.v1i.2316