Error correction and decomposition method for forecast of interval-valued stock price time series

CHEN Wei, XU Huilin, WANG Shouyang, SUN Shaolong

Systems Engineering - Theory & Practice ›› 2023, Vol. 43 ›› Issue (2) : 383-397.

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Systems Engineering - Theory & Practice ›› 2023, Vol. 43 ›› Issue (2) : 383-397. DOI: 10.12011/SETP2021-2490

Error correction and decomposition method for forecast of interval-valued stock price time series

  • CHEN Wei1, XU Huilin2, WANG Shouyang3,4,5, SUN Shaolong2
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Abstract

Compared with point data, interval data can better grasp the internal structural characteristics of financial markets from a global perspective. However, the existing prediction research on interval data only focuses on the single prediction of error series or the preprocessing of original series, and the methods usually cannot fully extract the main features of interval stock price time series. Therefore, this paper proposes an error correction and decomposition method for forecast of interval-valued stock price time series. In view of the role of the error series in the combination forecasting model, we first use Ljung-Box test and machine learning model to test and modify the interval-valued error series generated by the original series. Then, bivariate empirical mode decomposition technique is used to decompose the corrected error series into multiple intrinsic mode functions (IMFs) and a residual. Then, a single machine learning model is used to predict each IMFs component and residual except IMF1 component. Finally, the predicted values of the original series and the error series are aggregated to reconstruct the predicted values of the interval stock price. Furthermore, on the basis of the proposed method, we build an interval-valued stock price forecasting model based on error correction and decomposition, and use real stock market data for empirical analysis. Experimental results show that the proposed method is superior to some traditional methods in prediction accuracy.

Key words

error correction / error decomposition / interval prediction / financial prediction / machine learning

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CHEN Wei , XU Huilin , WANG Shouyang , SUN Shaolong. Error correction and decomposition method for forecast of interval-valued stock price time series. Systems Engineering - Theory & Practice, 2023, 43(2): 383-397 https://doi.org/10.12011/SETP2021-2490

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Funding

Fundamental Research Funds for the Central Universities (SK2021007); National Natural Science Foundation of China (72101197, 71988101, 72071134)
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