Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2014 Jun 27;9(6):e101113.
doi: 10.1371/journal.pone.0101113. eCollection 2014.

A feature fusion based forecasting model for financial time series

Zhiqiang Guo  1 , Huaiqing Wang  2 , Quan Liu  1 , Jie Yang  1
Affiliations

A feature fusion based forecasting model for financial time series

Zhiqiang Guo et al. PLoS One. .

Abstract

Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. The input and output data of AICA-SVR model.
Figure 2
Figure 2. Auto ICA regression model (AICA-SVR).
Figure 3
Figure 3. The input and output data of the MICA-SVR model.
Figure 4
Figure 4. Multi-variable ICA regression model (MICA-SVR).
Figure 5
Figure 5. ICA-CCA regression model (ICA-CCA-SVR).
Figure 6
Figure 6. Shows the curve versus the variation of dimensions and the proposed method consistently outperforms the other methods in the Shanghai stock market index.
Figure 7
Figure 7. Shows the curve versus the variation of dimensions and the proposed method consistently outperforms the other methods in the Dow Jones index.
Figure 8
Figure 8. The actual Shanghai stock market index and its predicted values from ICA-CCA-SVR, MICA-SVR, and A ICA-SVR.
Figure 9
Figure 9. The actual Dow Jones index and its predicted values from ICA-CCA-SVR, MICA-SVR, and AICA-SVR.
See this image and copyright information in PMC

References

    1. Felsen J (1975) Learning pattern recognition techniques applied to stock market forecasting. IEEE T SYS MAN CY 5(6): 583–594.
    1. Verleysen M, François D (2005) The curse of dimensionality in data mining and time series prediction.In Proceeding of the 18th International Conference on Artificial Neural Networks:computational Intelligence and Bioinspired Systems, Springer-Verlag Berlin, Heidelberg, pp 758–770.
    1. Ince H, Trafalis T (2007) Kernel principal component analysis and support vector machines for stock price prediction. IIE Transactions 39(6): 629–637.
    1. Fu T, Chung F, Ng V, Luk R (2001) Pattern discovery from stock time series using self-organizing maps. In Workshop Notes of KDD2001 Workshop on Temporal Data Mining, pp 27–37.
    1. Keogh E, Chakrabati K, Pazzani M, Mehrota S (2000) Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems 3(3): 263–286.

Publication types

LinkOut - more resources