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Financial time series forecasting using LPP and SVM optimized by PSO

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Abstract

In this paper, a predicting model is constructed to forecast stock market behavior with the aid of locality preserving projection, particle swarm optimization, and a support vector machine. First, four stock market technique variables are selected as the input feature, and a slide window is used to obtain the input raw data of the model. Second, the locality preserving projection method is utilized to reduce the dimension of the raw data and to extract the intrinsic feature to improve the performance of the predicting model. Finally, a support vector machine optimized using particle swarm optimization is applied to forecast the next day’s price movement. The proposed model is used with the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better than other models in the areas of prediction accuracy rate and profit.

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Acknowledgments

This research was supported by the Fundamental Research Funds for the Central Universities, China (Grant no. 2011-IV-058).

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Authors and Affiliations

  1. School of Information Engineering, Wuhan University of Technology, Wuhan, China

    Guo Zhiqiang & Liu Quan

  2. Department of Information Systems, City University of Hong Kong, Hong Kong, Hong Kong

    Guo Zhiqiang & Wang Huaiqing

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  1. Guo Zhiqiang
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  2. Wang Huaiqing
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  3. Liu Quan
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Correspondence to Guo Zhiqiang.

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Communicated by V. Loia.

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Zhiqiang, G., Huaiqing, W. & Quan, L. Financial time series forecasting using LPP and SVM optimized by PSO. Soft Comput 17, 805–818 (2013). https://doi.org/10.1007/s00500-012-0953-y

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