Research on the Prediction of Shenzhen Growth Enterprise Market Price Index Based on EMD-ARIMA Model

Tianhua Li (), Shaowei Qu () and Gaoping Huang ()
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Tianhua Li: University of Science and Technology Beijing
Shaowei Qu: University of Science and Technology Beijing
Gaoping Huang: University of Science and Technology Beijing

A chapter in LISS 2020, 2021, pp 783-795 from Springer

Abstract: Abstract Under the background of mass entrepreneurship and innovation, it is of great practical and theoretical significance to predict the Shenzhen Growth Enterprise Market (GEM) price index. In view of the nonlinearity and the non-stationarity of GEM price index a prediction method based on empirical mode decomposition (EMD) and Autoregressive Integrated Moving Average (ARIMA) model is studied. Firstly, the EMD is used to stabilize GEM price index data to make GEM price index data more regular and improve the non-linear and non-stationary characteristics of GEM price index data. Then, the ARIMA model is used to model and predict the decomposed data. The model accuracy evaluation index results of the EMD-ARIMA model in this paper are lower than that of the ARIMA model and the average error rate of the prediction results is also lower than the ARIMA model. The results show that the method proposed in this paper is more accurate than that of direct prediction by only using ARIMA model, which indicates that the EMD-ARIMA method proposed in this paper has higher generalization ability and prediction accuracy.

Keywords: Data mining; Time series; The growth enterprise market price index; EMD-ARIMA (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-33-4359-7_54

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DOI: 10.1007/978-981-33-4359-7_54

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