Stock Price Prediction based on SSA and SVM | Zendy

Fenghua Wen | Zendy; Jihong Xiao | Zendy; Zhifang He | Zendy; Xu Gong | Zendy

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Stock Price Prediction based on SSA and SVM

Author(s) -

Fenghua Wen,

Jihong Xiao,

Zhifang He,

Xu Gong

Publication year - 2014

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2014.05.309

Subject(s) - support vector machine , computer science , stock price , artificial intelligence , singular spectrum analysis , stock market , machine learning , stock (firearms) , pattern recognition (psychology) , data mining , series (stratigraphy) , singular value decomposition , mechanical engineering , paleontology , horse , engineering , biology

This paper, using the singular spectrum analysis (SSA), decomposes the stock price into terms of the trend, the market fluctuation, and the noise with different economic features over different time horizons, and then introduce these features into the support vector machine (SVM) to make price predictions. The empirical evidence shows that, compared with the SVM without these price features, the combination predictive methods-the EEMD-SVM and the SSA-SVM, which combine the price features into the SVMs perform better, with the best prediction to the SSA-SVM

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