Original scientific paper

https://doi.org/10.17559/TV-20200430034527

A Grey Model-Least Squares Support Vector Machine Method for Time Series Prediction

Ai Wang ; University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing, China
Xuedong Gao ; University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing, China

Abstract

In this study, the authors aim to solve the time series prediction problem through pre-predicting multiple influence factors of the target sequence. Focusing on two pre-prediction approaches of influence factors (i.e., factors driven approach and time driven approach), we propose a time series prediction method based on the least squares support vector machine and grey model (GM-LSSVM). This method could improve the prediction precision of the target time series by differentiating the data characteristics of influence factors. A case study is put forward to predict China's economy from the perspective of system innovation and technological innovation. We selected public statistics data from 2005 to 2014 from the national bureau. The numerical experiment results illustrate that the accuracy of the GM-LSSVM is able to reach 95%, which proves the effectiveness of our proposed method in practice.

Keywords

economic growth; grey model; least squares support vector machine; time series prediction

Hrčak ID:

242312

URI

https://hrcak.srce.hr/242312

Publication date:

15.8.2020.

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