Forecasting Method of Stock Market Volatility in Time Series Data Based on Mixed Model of ARIMA and XGBoost
Wang, Yan; Guo, Yuankai
2020-03
Source PublicationCHINA COMMUNICATIONS
ISSN1673-5447
Volume17Issue:3Pages:205-221
AbstractStock price forecasting is an important issue and interesting topic in financial markets. Because reasonable and accurate forecasts have the potential to generate high economic benefits, many researchers have been involved in the study of stock price forecasts. In this paper, the DWT-ARIMA-GSXGB hybrid model is proposed. Firstly. the discrete wavelet transfonn is used to split the data set into approximation and error parts. Then the ARIMA (0, 1, 1), ARIMA (1. 1, 0), ARIMA (2, 1. 1) and ARIMA (3, 1. 0) models respectively process approximate partial data and the improved xgboost model (GSXGB) handles error partial data. Finally, the prediction results are combined using wavelet reconstruction. According to the experimental comparison of 10 stock data sets, it is found that the errors of DWT-ARIMA-GSXGB model are less than the four prediction models of ARIMA, XGBoost, GSXGB and DWT-ARIMA-XGBoost. The simulation results show that the DWT-ARIMA-GSXGB stock price prediction model has good approximation ability and generalization ability, and can fit the stock index opening price well. And the proposed model is considered to greatly improve the predictive performance of a single ARIMA model or a single XGBoost model in predicting stock prices.
Keywordhybrid model discrete wavelet transform ARIMA XGBoost grid search stock price forecast
Indexed BySCI
Language英语
WOS Research AreaTelecommunications
WOS SubjectTelecommunications
WOS IDWOS:000522830300018
PublisherCHINA INST COMMUNICATIONS
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.lut.edu.cn/handle/2XXMBERH/76640
Collection计算机与通信学院
Corresponding AuthorGuo, Yuankai
AffiliationLanZhou Univ Technol, Coll Comp & Commun, Lanzhou 730050, Peoples R China
First Author AffilicationColl Comp & Commun
Corresponding Author AffilicationColl Comp & Commun
First Signature AffilicationColl Comp & Commun
Recommended Citation
GB/T 7714
Wang, Yan,Guo, Yuankai. Forecasting Method of Stock Market Volatility in Time Series Data Based on Mixed Model of ARIMA and XGBoost[J]. CHINA COMMUNICATIONS,2020,17(3):205-221.
APA Wang, Yan,&Guo, Yuankai.(2020).Forecasting Method of Stock Market Volatility in Time Series Data Based on Mixed Model of ARIMA and XGBoost.CHINA COMMUNICATIONS,17(3),205-221.
MLA Wang, Yan,et al."Forecasting Method of Stock Market Volatility in Time Series Data Based on Mixed Model of ARIMA and XGBoost".CHINA COMMUNICATIONS 17.3(2020):205-221.
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