Stock Price Prediction based on SSA and SVM

Jihong Xiao · Zhifang He · Xu Gong

108 citations0/0 files storedProcedia Computer Science

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Time Series Analysis and Forecasting

Abstract

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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References

2007A new approach for crude oil price analysis based on Empirical Mode Decomposition 2008Short term forecasting with support vector machines and application to stock price prediction

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